WO2008074155A1 - Pharmaceutical-grade botanical drug compositions and methods for manufacturing same - Google Patents
Pharmaceutical-grade botanical drug compositions and methods for manufacturing same Download PDFInfo
- Publication number
- WO2008074155A1 WO2008074155A1 PCT/CA2007/002344 CA2007002344W WO2008074155A1 WO 2008074155 A1 WO2008074155 A1 WO 2008074155A1 CA 2007002344 W CA2007002344 W CA 2007002344W WO 2008074155 A1 WO2008074155 A1 WO 2008074155A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- batches
- batch
- botanical
- units
- active ingredients
- Prior art date
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
- A61K36/00—Medicinal preparations of undetermined constitution containing material from algae, lichens, fungi or plants, or derivatives thereof, e.g. traditional herbal medicines
Definitions
- This invention relates to botanical compositions comprising medicinally useful naturally occurring active ingredients. More particularly, this invention relates to quantitative methods for combining multiple batches of botanical materials into compositions that comprise a plurality of selected active ingredients wherein each active ingredient is provided within a target concentration range.
- Botanical materials have been used historically for the extraction of chemical components to produce extracts as health foods, dietary supplements and as drugs for the treatment of a large variety of medical conditions.
- Botanical extracts produced by modern extraction processes are typically complex mixtures containing many chemical components at various concentrations. Variations in the chemical compositions of the extracts are often attributable to a number of underlying causes including, but not limited to, variations in the growing conditions of the plant materials, variations in the post-harvest handling conditions, and variations in the extraction process.
- United States Patent No. 6,379,714 disclosed methods for the use of bioassay activities to monitor the consistency of botanical materials for the purpose of produce botanical extracts suitable for use to treat diseases.
- the exemplary embodiments of the present invention are directed to methods for combining multiple batches of materials extracted from one or more botanical sources into pharmaceutical-grade botanical drug compositions comprising a plurality of selected active ingredients wherein each active ingredient is provided within a specified target concentration range, and also, to pharmaceutical-grade botanical drug compositions thereby provided.
- a method comprising a first step of characterizing each batch from a set of batches of materials extracted from one or more botanical sources, to detect and to quantify therein individual active ingredients useful for medicinal purposes thereby producing at least one set of data for each batch from the set of batches, and a second step comprising mathematically processing, analyzing, computing and optimizing the sets of data for producing a formula for combining sub-samples from the set of batches of materials extracted from a botanical source to provide a pharmaceutical- grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
- the botanical source is a plant species or a plant variety comprising at least one active ingredient known for medicinal properties.
- the at least one active ingredient is associated with materials extractable from plant parts said plant species or plant variety using processes known to those skilled in these arts.
- the plant parts from which the materials may be extracted comprise at least one of roots, rhizomes, tubers, bulbs, stems, bark, leaves, flowers, fruits, seeds, nuts, and parts thereof.
- the extracted materials may comprise solids as exemplified by powders, granules, particulates, or alternatively liquids as exemplified by oils, aqueous solutions, tinctures, or alternatively semi-solid substrates as exemplified by pastes and slurries.
- the first step comprises at least one analytical chemistry-based method configured for separating molecules as exemplified by chromatography, spectrophotometry, electrophoresis, mass spectromety, among others known to those skilled in these arts. It is also within the scope of the present invention to analyze and characterize the batches of materials with a selected set of analytical chemistry-based methods thereby providing a plurality of data sets for each batch of materials.
- the second step provided for mathematically computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, for the purpose of providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention may be performed with at least one algorithm exemplified by but not restricted to linear and non-linear constrained optimization approaches, multi-factorial calculus optimizations, linear and non-linear weighted least squares, non-negative least squares, linear and nonlinear distance programming, simplex algorithms, and other suitable approaches known to those skilled in the arts of mathematical optimization modeling.
- a software program incorporating therein at least one algorithm selected for mathematically computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, said software program configured for providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention.
- a software program incorporating therein a plurality of algorithms selected for mathematically computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, said software program configured for providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
- quality assurance testing methods and instructions for their use for detecting and quantifying the individual active ingredients provided in said botanical drug composition.
- Another exemplary embodiment of the present invention is directed to a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range, said botanical drug composition prepared according to a method comprising a first step of characterizing each batch from a set of batches of materials extracted from a botanical source, to detect and to quantify therein individual active ingredients useful for medicinal purposes thereby producing at least one set of data for each batch from the plurality of batches, a second step comprising mathematically processing, analyzing, computing and optimizing the sets of data for producing a formula for combining sub-samples from the plurality of batches of materials extracted from a botanical source, and then combining sub-samples from a plurality of batches selected from the set of batches, according to the formula provided by the second step to thereby provide a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
- the methods described in this present invention generally involve a first step wherein qualitative and quantitative analytical determinations are made in each of a plurality of manufacturing batches of extracts prepared from a supply of a botanical material, to determine the presence and the chemical composition of certain specific chemical components also referred to as active ingredients, considered by those skilled in these arts to be important for medicinal purposes.
- the second step in reference to a specified target composition comprising a set of active ingredients wherein each active ingredient falls within a selected concentration range, generally comprises the use of selected mathematical optimization algorithms for analyzing the qualitative and quantitative analytical data collected during the first step, to precisely determine the requisite quantity of each batch selected from the plurality of manufacturing batches required for pooling and mixing together to produce a botanical extract composition containing each of the active ingredients from the selected set of active ingredients within its selected concentration range as specified in Good Manufacturing Practices guidelines, for example as disclosed in the CGMP of botanical drugs product publication released in 2004 by the United States Food and Drug Administration, and related publications from Health Canada Natural Health Products Directorate. It is to be understood that "a source of botanical material" as used herein can refer to selected plant parts harvested from a selected plant species or plant varieties.
- Exemplary plant parts may comprise one or more of roots, rhizomes, tubers, bulbs, stems, bark, leaves, flowers, fruits, seeds, nuts, and parts thereof.
- a source of botanical material as used herein can refer to selected plant parts of a plant species or variety grown and harvested from diverse geographical locations, or alternatively, grown and harvested during different growing seasons.
- the identification and quantification of the chemical components in a botanical extract may be accomplished using a selection of single or multiple analytical chemical and biochemical assays known to those skilled in this arts, as exemplified by: (a) high pressure liquid chromatography (HPLC) with various modes of detection including by way of example liquid chromatography mass spectrometry (LC/MS), ultraviolet, fluorescence, reflective index, light scattering, electrochemical and other detectors, (b) gas chromatography (GC) with various modes of detection including by way of example gas chromatography mass spectrometry (GC/MS), Hall electroconductivity, flame ionization, electron capture, and other detectors,
- HPLC high pressure liquid chromatography
- LC/MS liquid chromatography mass spectrometry
- GC gas chromatography
- Hall electroconductivity flame ionization
- electron capture and other detectors
- capillary electrophoresis (d) capillary electrophoresis (CE) with a variety of detectors including ultraviolet, fluorescence, reflective index, light scattering, electrochemical and other detectors, and other techniques.
- Quantified data or alternatively relative percent abundance data of all observed major and minor chemical components are determined for each batch of extract from plurality of batches prepared from a source or alternatively multiples sources of a botanical material. Following the identification and quantification of each specific chemical component considered to be useful for medicinal purposes, mathematical constrained optimization functions are then used to provide simultaneous optimization determinations and to derive a mathematical optimal solution of the quantity of each manufacturing batch from a plurality of batches to produce a formula for combining the individual extract batches into a composition that comprises the set of desired active ingredients wherein each active ingredient is present within a selected target range.
- the second step of the present invention can be practiced with a variety of mathematical optimization algorithms as exemplified by linear and non-linear constrained optimization approaches, multi-factorial calculus optimizations, linear and non-linear weighted lease squares, non-negative least squares, linear and nonlinear distance programming, simplex algorithm, and other relevant approaches previously described by others skilled in the art of mathematical optimization modeling.
- the input of chemical composition data relating to specific chemical components for analysis, manipulation and computation by selected mathematical optimization algorithms can be conveniently carried out with the use of selected computer software programs developed using commonly programming languages previously described by others skilled in the art of writing computer programs.
- Such software programs are then used to mathematically determine the proportion of extracts from each production batch and specify the number of production batches required to be combined to produce an extract with a chemical composition matching the composition of each component of a desirable multiple component botanical extract.
- the present invention may be used to combine and blend a plurality of batches (e.g., five batches or more) of extracts prepared from a single source or a plurality of sources of a botanical material to produce a composition configured to provide a selected plurality of active ingredients extracted from the botanical material wherein the concentration each active ingredient provided falls within a specified target range.
- the known abundance of major and minor chemical components from five or more production batches of an extract can be compounded in a prescribed proportion calculated from the selected mathematical program to repeated, consistently and reproducibly provide an extract composition that meets and satisfies chemical composition specifications.
- the mathematical program relies on the quantified chemical composition of a number of major and minor components in each of a plurality of production batches of an extract and simultaneously computes the required quantities of specific batches of the extract to provide a final extract composition meeting a specific chemical specification.
- Table 1 lists the concentration of each of eight active ingredients (i.e., ai) in each often representative batches of medicinally useful extracts from a botanical material. It is evident that ai#5 is present in large quantities in all 10 batches (i.e., over 67%), while ai#3 and ai#4 are present in lesser but significant quantities in 9 batches but missing in batch 10. The remaining 7 ai#s are sporadically present in the 10 batches in unpredictably variable concentrations.
- the present invention can be used to predictably and reproducibly blend variable pluralities of these exemplary batches to provide a consistent multi-batched composition comprising the eight individual active ingredients wherein the concentration of each active ingredient is within a target concentration range.
- an "ideal theoretical" composition can be defined as one that is produced by blending together equal quantities of each of the ten manufacturing batches to produce a homogenous mixture of the extracts.
- the composition of this ideal mixture would comprise each of the 8 active ingredients at the concentrations listed in Table 2.
- This ideal composition of a heterogeneous botanical material may serve as a reference standard in the process of standardization during subsequent manufactured batches of materials to ensure batch-to-batch consistency.
- Another application of this ideal composition may serve as a reference clinical study material for the establishment of clinical efficacy data of a botanical material, such that the composition of the study material can be manufatured in a reproducible manner in support of product development activities ranging from clinical studies to marketed products.
- the component-batch matrix can be input to the program according to the following manner: i.e.
- the quantity unit should be weight and the upper and lower bounds defined as percentages in two vectors that together set a range for each of the components in the extracts.
- the lower vector for each component can be defined as 0.35; 2; 14; 3.4; 68; 3.5; 0.7; 2; whereas the upper vector can be defined as 0.4; 2.3; 15.6; 4; 77; 4; 0.8; 2.2.
- active ingredient 1 should make up 0.35-0.4% of the final batch.
- Active ingredient 2 should make up 2.0-2.3% of the final batch.
- Active ingredient 3 should make up 14.0-15.6% of the final batch.
- the output of the model provides a mathematically optimized formula for the requisite quantities of each of the batches for producing a botanical composition comprising the eight active ingredients with each ingredient falling within its target concentration range.
- the said mathematical modeling can determine an optimized formula for the number of batches of materials necessary to produce a composition having the desired active ingredients at the target concentration levels.
- Tables 4 - 6 illustrate for different pluralities of batch combinations, the effects of variation tolerances on the total numbers of (a) possible batch combinations, (b) the total number of compositions that could be formulated from each batch combination, and (c) the numbers of combinations that would provide acceptable compositions.
- Table 4 Relative allowable variation for each active ingredient set at + 5%.
- Table 5 Relative allowable variation for each active ingredient set at + 10%.
- Tables 4 to 6 show that while it is possible to blend the 10 individual batches together in a total of 1 ,012 combinations, as the tolerance for the concentration variation of each active ingredient is increased from 5% (Table 4) to 10% (Table 5) to 15% (Table 6), the number of compositions prepared from the 10 batches increases from 103 to 280 to 394 respectively.
- the variation tolerance is set at + 5% (Table 4)
- only 51% of the compositions prepared from 8 batches will satisfy the target concentration ranges for all 8 active ingredients, while 80% of the compositions prepared from 9 batches will satisfy the target concentration ranges for all 8 active ingredients.
- the methods of the present invention are useful for optimizing the selection of numbers of batches for blending together to produce a target composition so as to predictably, consistently and reproducibly provide a target composition comprising a plurality of active ingredients wherein each active ingredient is present within a target concentration range.
- concentrations of each of the 8 active ingredients, each within a ⁇ 10% acceptable deviation from the target composition, derived from the above proportions comprising 9 batches are as follows:
- concentrations of each of the 8 active ingredients, each within a ⁇ 10% acceptable deviation from the target composition, derived from the above proportions comprising of 8 batches are as follows:
- concentrations of each of the 8 active ingredients, each within a ⁇ 10% acceptable deviation from the target composition, derived from the above proportions comprising of 7 batches are as follows:
- concentrations of each of the 8 active ingredients, each within a ⁇ 10% acceptable deviation from the target composition, derived from the above proportions comprising of 6 batches are as follows:
- concentrations of each of the 8 active ingredients, each within a ⁇ 10% acceptable deviation from the target composition, derived from the above proportions comprising of 5 batches are as follows:
- concentrations of each of the 8 active ingredients, each within a +10% acceptable deviation from the target composition, derived from the above proportions comprising of 4 batches are as follows:
- the mathematical model was evolved to accept data entries for mathematical modeling inclusive of the pooled minor constituents and to compare the concentration of each major constituent to the pooled minor constituents, thereby enabling determination of the material mass balance of each batch of plant material analyzed, and their subsequent use in manufacturing blending of the materials from multiple sub-batches of materials.
- the eight batches of plant extracts were produced by extracting the dried powdered snow lotus herb material in 70% methanol for 30 minutes at 40° C. The methanol was then removed by evaporation thereby producing solids comprising the chemical constituents extracted from the herb material. The solids were then re-dissolved in fresh 70% methanol to an arbitrary selected concentration, after which, the liquid plant extract was analyzed using mass spectrometry. In each study, the number of constituents were noted and their relative concentrations determined from the mass spectrometry analyses.
- the minimum threshold level for individual constituents extracted from the 8 batches of snow lotus herb in the first study was set at a relative % level of 0.74. A total of 17 individual constituents that exceeded this threshold were routinely extracted from the 8 batches (Table 7).
- the quality control standard selected for this study was that concentration of each of the 17 individual constituents in the final product produced from blending the 8 batches together, was allowed to deviate over a range of + 54% compared against a predetermined target concentration for each of the 17 constituents.
- the mathematical model Based on data input from Table 7 and the selected quality control standard, the mathematical model generated a blending formula that specified
- Table 8 Actual relative % of each constituent compared to the target.
- the minimum threshold level for individual constituents extracted from the 8 batches of snow lotus herb in the second study was set at a relative % level of 1.5. A total of 10 individual constituents that exceeded this threshold were routinely extracted from the 8 batches (Table 9).
- the quality control standard selected for this study was that concentration of each of the 10 individual constituents in the final product produced from blending the 8 batches together, was allowed to deviate + 30% compared against a predetermined target concentration for each of the 10 constitutents.
- the mathematical model Based on data input from Table 9 and the selected quality control standard, the mathematical model generated a blending formula that specified
- the minimum threshold level for individual constituents extracted from the 8 batches of snow lotus herb in the third study was set at a relative % level of 0.14. A total of 22 individual constituents that exceeded this threshold were routinely extracted from the 8 batches (Table 9).
- the quality control standard selected for this study was that concentration of each of the 22 individual constituents in the final product produced from blending the 8 batches together, was allowed to deviate ⁇ 14% compared against a predetermined target concentration for each of the 22 constitutents.
- the mathematical model Based on data input from Table 11 and the selected quality control standard, the mathematical model generated a blending formula that specified
- Table 12 Actual relative % of each constituent compared to the target.
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Alternative & Traditional Medicine (AREA)
- Biotechnology (AREA)
- Botany (AREA)
- Medical Informatics (AREA)
- Medicinal Chemistry (AREA)
- Microbiology (AREA)
- Mycology (AREA)
- Natural Medicines & Medicinal Plants (AREA)
- Pharmacology & Pharmacy (AREA)
- Epidemiology (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Pharmaceuticals Containing Other Organic And Inorganic Compounds (AREA)
Abstract
A method for producing a pharmaceutical-grade botanical drug composition comprising a selected set of blended-together active ingredients sourced from multiple batches of plant extracts, wherein each active ingredient is provided within a specified concentration range. The first step comprises analyzing each batch from a set of batches of botanical extracts to produce a related data set that identifies and quantifies the active ingredient composition of the batch. The second step comprises mathematically processing, analyzing, computing and optimizing the sets of data generated in the first step, to produce a formula for combining sub-samples from the set of batches of botanical extracts to provide a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range. The third step comprises blending together sub-samples from selected batches of the botanical extracts to produce the pharmaceutical-grade botanical drug composition.
Description
PHARMACEUTICAL-GRADE BOTANICAL DRUG COMPOSITIONS AND METHODS FOR MANUFACTURING SAME
TECHNICAL FIELD
This invention relates to botanical compositions comprising medicinally useful naturally occurring active ingredients. More particularly, this invention relates to quantitative methods for combining multiple batches of botanical materials into compositions that comprise a plurality of selected active ingredients wherein each active ingredient is provided within a target concentration range.
BACKGROUND ART Botanical materials have been used historically for the extraction of chemical components to produce extracts as health foods, dietary supplements and as drugs for the treatment of a large variety of medical conditions. Botanical extracts produced by modern extraction processes are typically complex mixtures containing many chemical components at various concentrations. Variations in the chemical compositions of the extracts are often attributable to a number of underlying causes including, but not limited to, variations in the growing conditions of the plant materials, variations in the post-harvest handling conditions, and variations in the extraction process.
United States Patent No. 6,379,714 disclosed methods for the use of bioassay activities to monitor the consistency of botanical materials for the purpose of produce botanical extracts suitable for use to treat diseases.
DISCLOSURE OF THE INVENTION
The exemplary embodiments of the present invention are directed to methods for combining multiple batches of materials extracted from one or more botanical sources into pharmaceutical-grade botanical drug compositions comprising a plurality of selected active ingredients wherein each active ingredient is provided
within a specified target concentration range, and also, to pharmaceutical-grade botanical drug compositions thereby provided.
According to one exemplary embodiment of the present invention, there is provided a method comprising a first step of characterizing each batch from a set of batches of materials extracted from one or more botanical sources, to detect and to quantify therein individual active ingredients useful for medicinal purposes thereby producing at least one set of data for each batch from the set of batches, and a second step comprising mathematically processing, analyzing, computing and optimizing the sets of data for producing a formula for combining sub-samples from the set of batches of materials extracted from a botanical source to provide a pharmaceutical- grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
According to one aspect, the botanical source is a plant species or a plant variety comprising at least one active ingredient known for medicinal properties. The at least one active ingredient is associated with materials extractable from plant parts said plant species or plant variety using processes known to those skilled in these arts. The plant parts from which the materials may be extracted comprise at least one of roots, rhizomes, tubers, bulbs, stems, bark, leaves, flowers, fruits, seeds, nuts, and parts thereof. The extracted materials may comprise solids as exemplified by powders, granules, particulates, or alternatively liquids as exemplified by oils, aqueous solutions, tinctures, or alternatively semi-solid substrates as exemplified by pastes and slurries.
According to another aspect, the first step comprises at least one analytical chemistry-based method configured for separating molecules as exemplified by chromatography, spectrophotometry, electrophoresis, mass spectromety, among others known to those skilled in these arts. It is also within the scope of the present invention to analyze and characterize the batches of materials with a selected set of analytical chemistry-based methods thereby providing a plurality of data sets for each batch of materials.
According to a further aspect of the present invention, the second step provided for mathematically computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, for the purpose of providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention, may be performed with at least one algorithm exemplified by but not restricted to linear and non-linear constrained optimization approaches, multi-factorial calculus optimizations, linear and non-linear weighted least squares, non-negative least squares, linear and nonlinear distance programming, simplex algorithms, and other suitable approaches known to those skilled in the arts of mathematical optimization modeling. It is within the scope of the present invention to provide selected combination of algorithms for performing mathematical computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, for the purpose of providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
According to yet a further aspect of the present invention, there is provided a software program incorporating therein at least one algorithm selected for mathematically computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, said software program configured for providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention. It is within the scope of the present invention to provide a software program incorporating therein a plurality of algorithms selected for mathematically computing, comparing, analyzing, manipulating and optimizing the plurality of data sets generated during the first step, said software program configured for providing a formula for combining selected batches from the set of batches to provide a botanical drug composition of the present invention comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
According to another aspect of the invention, there is provided a process for combining a plurality of batches analyzed and characterized by the first step of the methods described herein, according to the formula provided by the second step of the methods described herein, thereby providing a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
According to a further aspect of the invention, there is provided quality assurance testing methods and instructions for their use, for detecting and quantifying the individual active ingredients provided in said botanical drug composition.
Another exemplary embodiment of the present invention is directed to a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range, said botanical drug composition prepared according to a method comprising a first step of characterizing each batch from a set of batches of materials extracted from a botanical source, to detect and to quantify therein individual active ingredients useful for medicinal purposes thereby producing at least one set of data for each batch from the plurality of batches, a second step comprising mathematically processing, analyzing, computing and optimizing the sets of data for producing a formula for combining sub-samples from the plurality of batches of materials extracted from a botanical source, and then combining sub-samples from a plurality of batches selected from the set of batches, according to the formula provided by the second step to thereby provide a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range.
BEST MODES FOR CARRYING OUT THE INVENTION
The methods described in this present invention generally involve a first step wherein qualitative and quantitative analytical determinations are made in each of a plurality of manufacturing batches of extracts prepared from a supply of a botanical material, to determine the presence and the chemical composition of certain specific chemical components also referred to as active ingredients, considered by those skilled in these arts to be important for medicinal purposes. The second step, in reference to a specified target composition comprising a set of active ingredients wherein each active ingredient falls within a selected concentration range, generally comprises the use of selected mathematical optimization algorithms for analyzing the qualitative and quantitative analytical data collected during the first step, to precisely determine the requisite quantity of each batch selected from the plurality of manufacturing batches required for pooling and mixing together to produce a botanical extract composition containing each of the active ingredients from the selected set of active ingredients within its selected concentration range as specified in Good Manufacturing Practices guidelines, for example as disclosed in the CGMP of botanical drugs product publication released in 2004 by the United States Food and Drug Administration, and related publications from Health Canada Natural Health Products Directorate. It is to be understood that "a source of botanical material" as used herein can refer to selected plant parts harvested from a selected plant species or plant varieties. Exemplary plant parts may comprise one or more of roots, rhizomes, tubers, bulbs, stems, bark, leaves, flowers, fruits, seeds, nuts, and parts thereof. Alternatively, a source of botanical material" as used herein can refer to selected plant parts of a plant species or variety grown and harvested from diverse geographical locations, or alternatively, grown and harvested during different growing seasons.
The identification and quantification of the chemical components in a botanical extract may be accomplished using a selection of single or multiple analytical chemical and biochemical assays known to those skilled in this arts, as exemplified by:
(a) high pressure liquid chromatography (HPLC) with various modes of detection including by way of example liquid chromatography mass spectrometry (LC/MS), ultraviolet, fluorescence, reflective index, light scattering, electrochemical and other detectors, (b) gas chromatography (GC) with various modes of detection including by way of example gas chromatography mass spectrometry (GC/MS), Hall electroconductivity, flame ionization, electron capture, and other detectors,
(c) nuclear magnetic resonance (NMR), and
(d) capillary electrophoresis (CE) with a variety of detectors including ultraviolet, fluorescence, reflective index, light scattering, electrochemical and other detectors, and other techniques.
Quantified data or alternatively relative percent abundance data of all observed major and minor chemical components are determined for each batch of extract from plurality of batches prepared from a source or alternatively multiples sources of a botanical material. Following the identification and quantification of each specific chemical component considered to be useful for medicinal purposes, mathematical constrained optimization functions are then used to provide simultaneous optimization determinations and to derive a mathematical optimal solution of the quantity of each manufacturing batch from a plurality of batches to produce a formula for combining the individual extract batches into a composition that comprises the set of desired active ingredients wherein each active ingredient is present within a selected target range.
The second step of the present invention can be practiced with a variety of mathematical optimization algorithms as exemplified by linear and non-linear constrained optimization approaches, multi-factorial calculus optimizations, linear and non-linear weighted lease squares, non-negative least squares, linear and nonlinear distance programming, simplex algorithm, and other relevant approaches previously described by others skilled in the art of mathematical optimization modeling.
The input of chemical composition data relating to specific chemical components for analysis, manipulation and computation by selected mathematical optimization algorithms can be conveniently carried out with the use of selected computer software programs developed using commonly programming languages previously described by others skilled in the art of writing computer programs. Alternatively, it is within the scope of the present invention for development of a software program comprising a plurality of interactive and cooperative mathematical algorithms. Such software programs are then used to mathematically determine the proportion of extracts from each production batch and specify the number of production batches required to be combined to produce an extract with a chemical composition matching the composition of each component of a desirable multiple component botanical extract.
The present invention may be used to combine and blend a plurality of batches (e.g., five batches or more) of extracts prepared from a single source or a plurality of sources of a botanical material to produce a composition configured to provide a selected plurality of active ingredients extracted from the botanical material wherein the concentration each active ingredient provided falls within a specified target range. The known abundance of major and minor chemical components from five or more production batches of an extract can be compounded in a prescribed proportion calculated from the selected mathematical program to repeated, consistently and reproducibly provide an extract composition that meets and satisfies chemical composition specifications. The mathematical program relies on the quantified chemical composition of a number of major and minor components in each of a plurality of production batches of an extract and simultaneously computes the required quantities of specific batches of the extract to provide a final extract composition meeting a specific chemical specification.
In an exemplary illustration of the methods of the present invention and the pharmaceutical-grade botanical compositions thereby prepared, Table 1 lists the concentration of each of eight active ingredients (i.e., ai) in each often representative batches of medicinally useful extracts from a botanical material. It is evident that
ai#5 is present in large quantities in all 10 batches (i.e., over 67%), while ai#3 and ai#4 are present in lesser but significant quantities in 9 batches but missing in batch 10. The remaining 7 ai#s are sporadically present in the 10 batches in unpredictably variable concentrations. The present invention can be used to predictably and reproducibly blend variable pluralities of these exemplary batches to provide a consistent multi-batched composition comprising the eight individual active ingredients wherein the concentration of each active ingredient is within a target concentration range.
Table 1.Concentration of eight active ingredients in each often batches of extracts from a botanical material
Active Batch 1 Batch 2 Batch 3 Batch 4 Batch 5 Batch 6 Batch 7 Batch 8 Batch 9 Batch 10 ingredient Wt % Wt % Wt % Wt % Wt , % Wt % Wt % Wt % Wt % Wt % ai #1 0.5 0.6% 0 0.0% 0.2 0.2% 0.1 0.1% 0.2 0.3% 0.1 0.1% 0.1 0.1% 0.1 0.1% 0.8 1.3% 0.7 0.9% ai #2 3.9 4.4% 0 0.0% 1.1 1.3% 0.8 0.9% 0.5 0.7% 0.3 0.4% 0.4 0.4% 0.5 0.5% 4 6.4% 5 6.6% ai #3 18 20.3% 14 16.5% 16 19.4% 17 18.8% 11 14.8% 10 13.6% 19 21.3% 19 20.5% 2.5 4.0% 0 0.0% ai #4 5.2 5.9% 4.6 5.4% 0.3 0.4% 2.9 3.2% 4.6 6.2% 5.7 7.7% 3.4 3.8% 3.6 3.9% 0 0.0% 0 0.0% ai #5 61 68.8% 57 67.1% 59 71.7% 64 70.8% 54 72.6% 55 74.6% 63 70.5% 66 71.4% 54 86.5% 51 67.1% ai #6 O 0.0% 6.8 8.0% 3.1 3.8% 2.4 2.7% 2.3 3.1% 1.5 2.0% 1.5 1.7% 1.8 1.9% 0 0.0% 12 15.8% ai #7 O 0.0% 0 0.0% 0.5 0.6% 0.6 0.7% 0.4 0.5% 0.2 0.3% 0.3 0.3% 0.3 0.3% 1.1 1.8% 2.3 3.0% ai #8 O 0.0% 2.6 3.1% 2.1 2.6% 2.6 2.9% 1.4 , 1.9% 0.9 1.2% 1.6 1.8% 1.2 1.3% 0 0.0% 5 6.6%
^o Total 88.6 100% 85 100% 82.3 100% 90.4 100% 74.4 100% 73.7 100% 89.3 100% 92.5 100% 62.4 100% 76 100%
As a starting point, an "ideal theoretical" composition can be defined as one that is produced by blending together equal quantities of each of the ten manufacturing batches to produce a homogenous mixture of the extracts. The composition of this ideal mixture would comprise each of the 8 active ingredients at the concentrations listed in Table 2. This ideal composition of a heterogeneous botanical material may serve as a reference standard in the process of standardization during subsequent manufactured batches of materials to ensure batch-to-batch consistency. Another application of this ideal composition may serve as a reference clinical study material for the establishment of clinical efficacy data of a botanical material, such that the composition of the study material can be manufatured in a reproducible manner in support of product development activities ranging from clinical studies to marketed products.
Table 2: Calculated ideal theoretical composition.
Active Batch 1 ingredient Wt (g) % ai #l 0.3 0.4 ai #2 1.8 2.4 ai #3 14.1 16.6 ai #4 3.8 4.6 ai #5 58.4 72.1 ai #6 3.9 4.9 ai #7 0.7 0.9 ai #8 2.2 2.7
Total 85.2 104.5
A least-distance constrained optimization algorithm was used to determine mathematical optimal quantities of each of the ten batches of extracts required to blend a model composition comprising the 8 active ingredients. The results are shown in Table 3.
Table 3: A mathematically optimized model composition.
Active Model Batch ingredient Wt (g) % ai #l 2.34 0.4 ai #2 13.53 2.2 ai #3 93.11 14.9 ai #4 22.76 3.6 ai #5 449.99 72.1 ai #6 24.31 3.9 ai #7 4.70 0.8 ai #8 13.26 2.1
Total 624.0 100
Comparison of the ideal composition from Table 2 with the model composition from Table 3, enabled the development of a formula with constraints of +/- 10% variation flexibility from an ideal value allowed by the mathematical model. This 10% variation flexibility permitted by the model is designed to produce extract materials of pharmaceutical grade of quality often available on the market with acceptance criteria of +/- 10% of label content.
For instance, the use of least distance constrained optimization programming as provided by MATLAB (open-source software available at: http://www.mathworks.com/products/matlab/), the component-batch matrix can be input to the program according to the following manner: i.e.
For instance, for extracts with eight active ingredients, the lower vector for each component can be defined as 0.35; 2; 14; 3.4; 68; 3.5; 0.7; 2; whereas the upper vector can be defined as 0.4; 2.3; 15.6; 4; 77; 4; 0.8; 2.2.
Therefore, active ingredient 1 should make up 0.35-0.4% of the final batch. Active ingredient 2 should make up 2.0-2.3% of the final batch. Active ingredient 3 should make up 14.0-15.6% of the final batch.
The output of the model provides a mathematically optimized formula for the requisite quantities of each of the batches for producing a botanical composition comprising the eight active ingredients with each ingredient falling within its target concentration range.
In another instance, the said mathematical modeling can determine an optimized formula for the number of batches of materials necessary to produce a composition having the desired active ingredients at the target concentration levels.
Tables 4 - 6 illustrate for different pluralities of batch combinations, the effects of variation tolerances on the total numbers of (a) possible batch combinations, (b) the total number of compositions that could be formulated from each batch combination, and (c) the numbers of combinations that would provide acceptable compositions.
Table 4: Relative allowable variation for each active ingredient set at + 5%.
# of batches used to Total # of % of combinations
Total # of possible formulate a compositions from the with acceptable batch combinations composition selected # of batches compositions at + 5%
9 10 8 80%
8 45 23 51%
7 120 37 30%
6 210 28 13%
5 252 7 2%
4 210 0 0%
3 120 0 0%
2 45 0%
Total 1,012 103
Table 5: Relative allowable variation for each active ingredient set at + 10%.
% of combinations
# of batches used to Total # of
Total # of possible with acceptable formulate a compositions from the batch combinations compositions at + composition selected # of batches 10%
9 10 9 90%
8 45 34 75%
7 12 71 61%
6 210 94 44%
5 252 60 23%
4 210 12 5%
3 120 0 0%
2 45 0 0%
Total 1,012 280
Table 6: Relative allowable variation for each active ingredient set at + 15%.
% of combinations
# of batches used to Total # of I
Total # of possible with acceptable formulate a compositions from the batch combinations compositions at + composition selected # of batches 15%
9 10 9 90%
8 45 38 : 84%
7 120 87 72%
6 210 123 58%
5 252 98 38%
4 210 36 17%
3 120 3 2%
2 45 0 0%
Total 1,012 394
The data in Tables 4 to 6 show that while it is possible to blend the 10 individual batches together in a total of 1 ,012 combinations, as the tolerance for the concentration variation of each active ingredient is increased from 5% (Table 4) to 10% (Table 5) to 15% (Table 6), the number of compositions prepared from the 10 batches increases from 103 to 280 to 394 respectively. However, when the variation tolerance is set at + 5% (Table 4), only 51% of the compositions prepared from 8 batches will satisfy the target concentration ranges for all 8 active ingredients, while 80% of the compositions prepared from 9 batches will satisfy the target concentration ranges for all 8 active ingredients. When the variation tolerance is set at + 10% (Table 5), only 61% of the compositions prepared from 7 batches will satisfy the target concentration ranges for all 8 active ingredients, while 75% of the compositions prepared from 8 batches will satisfy the target concentration ranges for all 8 active ingredients, and 90% of the compositions prepared from 9 batches will satisfy the target concentration ranges for all 8 active ingredients. When the variation tolerance is set at + 15% (Table 6), 58% of the compositions prepared from 6 batches will satisfy the target concentration ranges for all 8 active ingredients, 72% of the compositions prepared from 7 batches will satisfy the target concentration ranges for
all 8 active ingredients, 84% of the compositions prepared from 8 batches will satisfy the target concentration ranges for all 8 active ingredients, and 90% of the compositions prepared from 9 batches will satisfy the target concentration ranges for all 8 active ingredients. Those skilled in these arts will understand that the methods of the present invention are useful for optimizing the selection of numbers of batches for blending together to produce a target composition so as to predictably, consistently and reproducibly provide a target composition comprising a plurality of active ingredients wherein each active ingredient is present within a target concentration range.
The following examples illustrate the use of the methods of the present invention for selecting and blending together a subset of pluralities of batches from a set of 10 batches extracted from a source (or sources) of a botanical material wherein the formulae for achieving target concentrations of the 8 active ingredients are mathematically derived and optimized.
EXAMPLE 1
The mathematically derived and optimized formula for blending together sub-samples from each of 9 batches selected from the group of batches listed in Table 1 will provide a composition comprising the following combination:
> 88.60 units of Batch 1 (20.3%)
> 12.44 units of Batch 2 (2.8%)
> 47.54 units of Batch 3 (10.9%)
> 39.28 units of Batch 4 (9.0%)
> 52.14 units of Batch 5 (11.9%)
> 43.88 units of Batch 6 (10.0%)
> 42.77 units of Batch 7 (9.8%)
> 41.71 units of Batch 8 (9.6%)
> 68.30 units of Batch 10 (15.6%) Total weight = 436.67 units
Total percentage = 100%
The concentrations of each of the 8 active ingredients, each within a ±10% acceptable deviation from the target composition, derived from the above proportions comprising 9 batches are as follows:
> 1.58 units of ai #1 (0.4%)
> 10.32 units of ai #2 (2.4%)
> 68.01 units of ai #3 (15.6%)
> 17.18 units of ai #4 (3.9%)
> 307.60 units of ai #5 (70.4%)
> 18.65 units of ai #6 (4.3%)
> 3.29 units of ai #7 (0.8%)
> 10.04 units ai #8 (2.3%) Total weight = 436.67 units Total percentage = 100%
EXAMPLE 2
The mathematically derived and optimized formula for blending together sub-samples from each of 8 batches selected from the group of batches listed in Table 1 will provide a composition comprising the following combination:
> 60.61 units of Batch 2 (14.3%)
> 27.89 units of Batch 3 (6.6%)
> 47.85 units of Batch 4 (11.3%)
> 74.40 units of Batch 5 (17.6%)
> 53.86 units of Batch 7 (12.7%)
> 56.64 units of Batch 8 (13.4%)
> 60.75 units of Batch 9 (14.4%)
> 40.71 units of Batch 10 (9.6%) Total weight = 422.71 units Total percentage = 100%
The concentrations of each of the 8 active ingredients, each within a ±10% acceptable deviation from the target composition, derived from the above proportions comprising of 8 batches are as follows:
> 1.60 units of ai #1 (0.4%)
> 8.42 units of ai #2 (2.0%)
> 60.93 units of ai #3 (14.4%)
> 13.77 units of ai #4 (3.3%)
> 306.82 units of ai #5 (72.6%)
> 17.91 units of ai #6 (4.2%)
> 3.55 units of ai #7 (0.8%)
> 9.72 units of ai #8 (2.3%) Total weight = 422.71 units Total percentage = 100%
EXAMPLE 3
The mathematically derived and optimized formula for blending together sub-samples from each of 7 batches selected from the group of batches listed in Table 1 will provide a composition comprising the following combination:
> 67.84 units of Batch 2 (29.6%)
> 47.55 units of Batch 4 (20.7%)
> 36.39 units of Batch 5 (15.9%)
> 2.46 units of Batch 6 (1.1%)
> 7.20 units of Batch 7 (3.1%)
> 5.38 units of Batch 8 (2.3%)
> 62.40 units of Batch 9 (27.2%) Total weight = 229.22 units Total percentage = 100%
The concentrations of each of the 8 active ingredients, each within a ±10% acceptable deviation from the target composition, derived from the above proportions comprising of 7 batches are as follows:
> 0.97 units of ai #l (0.4%)
> 4.74 units of ai #2 (2.1%)
> 30.97 units of ai #3 (13.5%)
> 8.12 units of ai #4 (3.5%)
> 170.32 units of ai #5 (74.3%)
> 8.09 units of ai #6 (3.5%)
> 1.66 units of ai #7 (0.7%)
> 4.36 units of ai #8 (1.9%) Total weight = 229.22 units Total percentage = 100%
EXAMPLE 4
The mathematically derived and optimized formula for blending together sub-samples from each of 6 batches selected from the group of batches listed in Table 1 will provide a composition comprising the following combination:
> 11.70 units of Batch 4 (3.9%)
> 55.10 units of Batch 5 (18.2%)
> 70.19 units of Batch 7 (23.2%)
> 92.50 units of Batch 8 (30.6%)
> 36.90 units of Batch 9 (12.2%)
> 35.97 units of Batch 10 (11.9%) Total weight = 302.37 units
Total percentage = 100%
The concentrations of each of the 8 active ingredients, each within a ±10% acceptable deviation from the target composition, derived from the above proportions comprising of 6 batches are as follows:
> 1.14 units of ai #1 (0.4%)
> 6.02 units of ai #2 (2.0%)
> 45.76 units of ai #3 (15.1%)
> 10.05 units of ai #4 (3.3%)
> 219.87 units of ai #5 (72.7%)
> 10.67 units of ai #6 (3.5%)
> 2.65 units of ai #7 (0.9%)
> 6.20 units of ai #8 (2.0%) Total weight = 302.37 units Total percentage = 100%
EXAMPLE 5
The mathematically derived and optimized formula for blending together sub-samples from each of 5 batches selected from the group of batches listed in Table 1 will provide a composition comprising the following combination:
> 40.57 units of Batch 3 (25.9%)
> 10.88 units of Batch 4 (7.0%)
> 74.40 units of Batch 5 (47.6%)
> 21.70 units of Batch 9 (13.9%)
> 8.90 units of Batch 10 (5.7%) Total weight = 156.45 units Total percentage = 100%
The concentrations of each of the 8 active ingredients, each within a ±10% acceptable deviation from the target composition, derived from the above proportions comprising of 5 batches are as follows:
> 0.67 units of ai #l (0.4%)
> 3.11 units of ai #2 (2.0%)
> 21.80 units of ai #3 (13.9%)
> 5.10 units of ai #4 (3.3%)
> 115.54 units of ai #5 (73.8%)
> 5.52 units of ai #6 (3.5%)
> 1.37 units of ai #7 (0.9%)
> 3.33 units of ai #8 (2.1%) Total weight = 156.45 units Total percentage = 100%
EXAMPLE 6
The mathematically derived and optimized formula for blending together sub-samples from each of 4 batches selected from the group of batches listed in Table 1 will provide a composition comprising the following combination:
> 57.24 units of Batch 6 (28.1%)
> 92.50 units of Batch 8 (45.4%)
> 23.93 units of Batch 9 (11.8%)
> 29.96 units of Batch 10 (14.7%) Total weight = 203.62 units
Total percentage = 100%
The concentrations of each of the 8 active ingredients, each within a +10% acceptable deviation from the target composition, derived from the above proportions comprising of 4 batches are as follows:
> 0.76 units of ai #l (0.4%)
> 4.24 units of ai #2 (2.1%)
> 27.73 units of ai #3 (13.6%)
> 8.03 units of ai #4 (3.9%)
> 149.53 units of ai #5 (73.4%)
> 7.70 units of ai #6 (3.8%)
> 1.78 units of ai #7 (0.9%)
> 3.87 units of ai #8 (1.9%) Total weight = 203.62 units Total percentage = 100%
It is to be noted that the values in the previous examples were based on the actual weight of each chemical consituent in each batch, and that the mathematical model in those cases generated blending formulas that comprised target composition contents for each chemical constituent based on a w/w comparision with the other constituents identified in the initial analyses of the batches. The occurrence and quantities of minor plant constituents, i.e., those chemical constituents present in very small quantities relative to the major constituents identified and quantified, or those chemical constituents with no known biological medicinal activities were not considered by the model. We have noted that while the concentration of each minor constituent may be considered negligible in comparision to major constituents, the combined total of the minor constituents typically is of the same magnitude or greater than the individual major constituents. Accordingly, the mathematical model was evolved to accept data entries for mathematical modeling inclusive of the pooled minor constituents and to compare the concentration of each major constituent to the pooled minor constituents, thereby enabling determination of the material mass balance of each batch of plant material analyzed, and their subsequent use in manufacturing blending of the materials from multiple sub-batches of materials.
Three studies were conducted to assess and validate the robustness of the optimization model in processing actual data inputs comprising: (a) the presence or absence of multiple chemical constituents in each of multiple batches of plant extracts, and (b) the relative concentrations of each of the chemical constituents in each of the multiple batches of plant extracts. In each study, eight separate batches of plant extracts were prepared from dried and powedered Herba Saussureae involucratae, commonly known as: (a) snow lotus herb, and (b) tianshan xuelian. Snow lotus herb contains at least the following chemical constituents:
• l lβ,13-dihydrodehydrocostuslactone-8-O-[6'-O-acetyl-β-d-glucoside];
• 11 β, 13 -dihydrodehydrocostuslactone-8-O-β-d-glucoside;
• 3α-hydroxyl-l lβ,13-dihydrodehydrocosruslactone-8-O-β-d-glucoside;
• Acacetin;
• Apigenin;
• Apigenin-5,6-dimethoxy-flavone;
• Apigenin-6-methoxy-flavone;
• Apigenin-7-O— D-glucoside;
• Apigenin-7-O~D-rutinoside;
• Chlorogenic acid;
• Flavonoids;
• Hispidulin;
• Jaceoside;
• Kaempferide;
• Kaempferol;
• Kaempferol-3-O~D-glucoside;
• Luteolin
• Luteolin-7-O~D-glucoside;
• Quercetin;
• Rutin;
• Polysaccharides;
• Syringin;
• Umbelliferone;
• Umbelliferonglucoside.
The exact presence and content of each of the above constituents in extracts prepared from this plant material will vary considerably from batch to batch, between plant materials sourced from different growing locales, and will also be affected by processing methods.
In each of the following examples, the eight batches of plant extracts were produced by extracting the dried powdered snow lotus herb material in 70% methanol for 30 minutes at 40° C. The methanol was then removed by evaporation thereby producing solids comprising the chemical constituents extracted from the herb material. The solids were then re-dissolved in fresh 70% methanol to an
arbitrary selected concentration, after which, the liquid plant extract was analyzed using mass spectrometry. In each study, the number of constituents were noted and their relative concentrations determined from the mass spectrometry analyses.
EXAMPLE 6
The minimum threshold level for individual constituents extracted from the 8 batches of snow lotus herb in the first study was set at a relative % level of 0.74. A total of 17 individual constituents that exceeded this threshold were routinely extracted from the 8 batches (Table 7). The quality control standard selected for this study was that concentration of each of the 17 individual constituents in the final product produced from blending the 8 batches together, was allowed to deviate over a range of + 54% compared against a predetermined target concentration for each of the 17 constituents.
Based on data input from Table 7 and the selected quality control standard, the mathematical model generated a blending formula that specified
(13.0% Batch 1) + (13.6% Batch 2) + (9.5% Batch 3) + (11.4% Batch 4) + (18.2% Batch 5) + (14.3% Batch 6) + (10.0% Batch 7) + (9.9% Batch 8)
The 8 batches listed in Table 7 were blended together according to this formula, and then analyzed to determine actual relative % of each constituent for comparison to the target relative %. These data are shown in Table 8, and demonstrate that each constituent was within the set ± 54% quality control standard.
Table 7: Relative % concentration of 17 constituents extracted from snow lotus herb*
* zero values are missing components from each batch.
** "others" are minor components with a relative % concentration of less than 0.4%
Table 8: Actual relative % of each constituent compared to the target.
EXAMPLE 7
The minimum threshold level for individual constituents extracted from the 8 batches of snow lotus herb in the second study was set at a relative % level of 1.5. A total of 10 individual constituents that exceeded this threshold were routinely extracted from the 8 batches (Table 9). The quality control standard selected for this study was that concentration of each of the 10 individual constituents in the final product produced from blending the 8 batches together, was allowed to deviate +
30% compared against a predetermined target concentration for each of the 10 constitutents.
Table 9: Relative % concentration of 10 constituents extracted from snow lotus herb*
* zero values are missing components from each batch.
** "others" are minor components with a relative % concentration of less than 1.5%
Based on data input from Table 9 and the selected quality control standard, the mathematical model generated a blending formula that specified
(13.4% Batch 1) + (26.4% Batch 2) + (0.5% Batch 3) + (17.1% Batch 4) + (0.5% Batch 5) + (16.1% Batch 6) + (10.0% Batch 7) + (15.9% Batch 8)
The 8 batches listed in Table 9 were blended together according to this formula, and then analyzed to determine actual relative % of each constituent for comparison to the target relative %. These data are shown in Table 10, and demonstrate that each constituent was within the set + 30% quality control standard.
Table 10: Actual relative % of each constituent compared to the target.
EXAMPLE 8
The minimum threshold level for individual constituents extracted from the 8 batches of snow lotus herb in the third study was set at a relative % level of 0.14. A total of 22 individual constituents that exceeded this threshold were routinely extracted from the 8 batches (Table 9). The quality control standard selected for this study was that concentration of each of the 22 individual constituents in the final product produced from blending the 8 batches together, was allowed to deviate ± 14% compared against a predetermined target concentration for each of the 22 constitutents.
Based on data input from Table 11 and the selected quality control standard, the mathematical model generated a blending formula that specified
(8.5% Batch 1) + (7.1% Batch 2) + (21.2% Batch 3) + (1.6% Batch 4)
+ (30.1% Batch 5) + (2.4% Batch 6) + (12.0% Batch 7) + (17.0% Batch 8)Table 11 rRelative % concentration of 22 constituents extracted from snow lotus herb*
* zero values are missing components from each batch.
** "others" are minor components with a relative % concentration of less than 0.14%
The 8 batches listed in Table 11 were blended together according to this formula, and then analyzed to determine actual relative % of each constituent for comparison to the target relative %. These data are shown in Table 12, and demonstrate that each constituent was within the set + 14% quality control standard.
Table 12: Actual relative % of each constituent compared to the target.
Claims
1. A method of producing a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each active ingredient is provided within a specified concentration range, said method comprising: a first step wherein each batch from a set of batches of materials extracted from a botanical source, is analyzed to detect and to quantify therein individual active ingredients useful for medicinal purposes thereby producing at least one set of data for each batch from the set of batches; a second step comprising mathematically processing, analyzing, computing and optimizing the sets of data generated in the first step, said second step configured for producing a formula for combining sub-samples from the set of batches of materials to provide a pharmaceutical-grade botanical drug composition comprising a selected set of active ingredients wherein each ingredient is provided within a specified target concentration range; and a third step comprising blending together according to the formula provided by the second step, sub-samples from selected batches from the set of batches of extracted materials.
2. A method according to claim 1, wherein the first step comprises the analysis and quantification of individual active ingredients useful for medicinal purposes therein each batch from a set of batches of materials extracted from a plurality of botanical sources, thereby producing at least one set of data for each batch from the set of batches.
3. A method according to claim 1, wherein the first step comprises at least one analytical chemistry-based method configured for separating and quantifying medicinally useful compounds within each batch of materials extracted from the botanical source.
4. A method according to claim 1 , wherein the first step comprises a plurality of analytical chemistry-based methods configured for separating and quantifying medicinally useful compounds within each batch of materials extracted from the botanical source.
5. A method according to claim 1 , wherein the second step comprises at least one mathematical algorithm for processing, analyzing, computing and optimizing the sets of data generated in the first step.
6. A method according to claim 1 , wherein the second step comprises a plurality of mathematical algorithms for cooperatively and interactively processing, analyzing, computing and optimizing the sets of data generated in the first step.
7. A software program incorporating therein at least one mathematical algorithm for processing, analyzing, computing and optimizing the sets of data generated in the first step of the method of claim 1, said software program configured to provide a formula for blending together sub-samples of batches selected from the set of batches of materials extracted from the botanical source, to produce a pharmaceutical-grade botanical drug composition comprising a selected set of medicinally useful active ingredients wherein each active ingredient is provided within a specified concentration range.
8. A software program incorporating therein a plurality of mathematical algorithms for processing, analyzing, computing and optimizing the sets of data generated in the first step of the method of claim 1, said software program configured to provide a formula for blending together sub-samples of batches selected from the set of batches of materials extracted from the botanical source, to produce a pharmaceutical-grade botanical drug composition comprising a selected set of medicinally useful active ingredients wherein each active ingredient is provided within a specified concentration range.
9. A software program according to claim 7, wherein said at least one mathematical algorithm is selected from the group comprising linear and non-linear constrained optimization approaches, multi-factorial calculus optimizations, linear and non-linear weighted lease squares, non-negative least squares, linear and nonlinear distance programming, and simplex algorithms.
10. A software program according to claim 8, wherein said plurality of mathematical algorithms are selected from the group comprising linear and nonlinear constrained optimization approaches, multi-factorial calculus optimizations, linear and non-linear weighted lease squares, non-negative least squares, linear and nonlinear distance programming, and simplex algorithms.
11. A pharmaceutical-grade botanical drug composition comprising a selected set of medicinally useful active ingredients wherein each active ingredient is provided within a specified concentration range, said botanical drug composition produced according to the method of claim 1 or 2.
12. A pharmaceutical-grade botanical drug composition according to claim 11 , wherein said botanical source is selected from a group consisting of medicinal plants, herbs, and grains.
13. A pharmaceutical-grade botanical drug composition according to claim 11 , wherein said botanical source comprises plant parts selected from group comprising roots, rhizomes, tubers, bulbs, stems, bark, leaves, flowers, fruits, seeds, nuts, and parts thereof.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US87579406P | 2006-12-20 | 2006-12-20 | |
| US60/875,794 | 2006-12-20 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2008074155A1 true WO2008074155A1 (en) | 2008-06-26 |
Family
ID=39535951
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CA2007/002344 WO2008074155A1 (en) | 2006-12-20 | 2007-12-20 | Pharmaceutical-grade botanical drug compositions and methods for manufacturing same |
Country Status (1)
| Country | Link |
|---|---|
| WO (1) | WO2008074155A1 (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103058975A (en) * | 2012-10-16 | 2013-04-24 | 朱沂 | Application of 5,7-dihydroxy-4'-methoxy flavone preparation extracted from snow lotus in preparation of medicament for treating ischemic stroke |
| CN111979049A (en) * | 2020-08-26 | 2020-11-24 | 云南中烟工业有限责任公司 | Component directional preparation method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1999021006A1 (en) * | 1997-10-23 | 1999-04-29 | Pharmaprint, Inc. | Pharmaceutical grade valerian, black cohosh, vitex agnus-castus, bilberry and milk thistle |
| US6379714B1 (en) * | 1995-04-14 | 2002-04-30 | Pharmaprint, Inc. | Pharmaceutical grade botanical drugs |
| WO2003037250A2 (en) * | 2001-10-26 | 2003-05-08 | Phytoceutica, Inc. | Matrix methods for analyzing properties of botanical samples |
-
2007
- 2007-12-20 WO PCT/CA2007/002344 patent/WO2008074155A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6379714B1 (en) * | 1995-04-14 | 2002-04-30 | Pharmaprint, Inc. | Pharmaceutical grade botanical drugs |
| WO1999021006A1 (en) * | 1997-10-23 | 1999-04-29 | Pharmaprint, Inc. | Pharmaceutical grade valerian, black cohosh, vitex agnus-castus, bilberry and milk thistle |
| WO2003037250A2 (en) * | 2001-10-26 | 2003-05-08 | Phytoceutica, Inc. | Matrix methods for analyzing properties of botanical samples |
Non-Patent Citations (2)
| Title |
|---|
| KIMURA I. AND TSUNEKI H.: "Theoretical pharmacological effects in single herb and polyherbal therapies: influences of processing on the constituents and their synergistic/modulatory interactions", JOURNAL OF TRADITIONAL MEDICINES, vol. 22, no. 1, 2005, pages 1 - 3 * |
| LIU Y. ET AL.: "Study of blending for the extracts of herbal plants", CHINESE JOURNAL OF CHROMATOGRAPHY, vol. 24, no. 2, March 2006 (2006-03-01), pages 117 - 121, XP022857178, DOI: doi:10.1016/S1872-2059(06)60006-0 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103058975A (en) * | 2012-10-16 | 2013-04-24 | 朱沂 | Application of 5,7-dihydroxy-4'-methoxy flavone preparation extracted from snow lotus in preparation of medicament for treating ischemic stroke |
| CN111979049A (en) * | 2020-08-26 | 2020-11-24 | 云南中烟工业有限责任公司 | Component directional preparation method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hossain et al. | Constituents of the essential oil from different brands of Syzigium caryophyllatum L by gas chromatography–mass spectrometry | |
| López-Hidalgo et al. | Phytochemical composition and variability in Quercus ilex acorn morphotypes as determined by NIRS and MS-based approaches | |
| Donno et al. | Bud Extracts as New Phytochemical Source for Herbal Preparations—Quality Control and Standardization by Analytical Fingerprint | |
| Winkler et al. | Total reflection X‐ray fluorescence analysis of elemental composition of herbal infusions and teas | |
| Natsir et al. | Amino acid and mineral composition of Moringa oleivera leaves extract and its bioactivity as antioxidant | |
| Li et al. | Entirely control the quality consistency of Rong’e Yishen oral liquid by both quantified profiling and quantitative analysis of multi-components by single marker method | |
| Raymond et al. | GC-MS method validation and levels of methyl eugenol in a diverse range of tea tree (Melaleuca alternifolia) oils | |
| Liu et al. | Effects of growing location on the contents of main active components and antioxidant activity of Dasiphora fruticosa (L.) Rydb. by chemometric methods | |
| Veit | Quality requirements for medicinal cannabis and respective products in the European Union–Status quo | |
| WO2008074155A1 (en) | Pharmaceutical-grade botanical drug compositions and methods for manufacturing same | |
| Hoffman et al. | Spice and herb fraud | |
| Liu et al. | A comprehensive comparative study for the authentication of the Kadsura crude drug | |
| Juranović Cindrić et al. | Metal characterization of white hawthorn organs and infusions | |
| Brown et al. | Determination of ginsenoside content in Panax ginseng CA Meyer and Panax quinquefolius L. root materials and finished products by high-performance liquid chromatography with ultraviolet absorbance detection: interlaboratory study | |
| Santana et al. | Evaluation of the mineral content in forage palm (Opuntia ficus-indica Mill and Nopalea cochenillifera) using chemometric tools | |
| PENG et al. | Evaluation of essential and toxic element concentrations in buckwheat by experimental and chemometric approaches | |
| Talebi et al. | Effects of nanoparticles treatments and salinity stress on the genetic structure and physiological characteristics of Lavandula angustifolia Mill. | |
| Chen et al. | Rapid discrimination and feature extraction of three chamaecyparis species by static-HS/GC–MS | |
| Wang et al. | Effects of phosphorus stress on the growth and secondary metabolism of Artemisia argyi | |
| Sarvananda et al. | Adulteration methods and current trends in authentic identification of botanical materials used for the pharmaceuticals | |
| Gutteridge et al. | The application of DNA molecular approaches for the identification of herbal medicinal products | |
| Sano et al. | Dynamic metabolic changes during fruit maturation in Jatropha curcas L. | |
| Fidan et al. | Antioxidant capacity and phylogenetic analysis of twenty native grape cultivars in Siirt province, Turkey | |
| Baccarin et al. | Influence of extraction parameters on hydroalcohol extracts of the stem bark of Rapanea ferruginea Mez using myrsinoic acid b as marker | |
| Hon et al. | Evaluation of a proficiency test on cadmium and lead in herbal material using assigned reference values |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 07855624 Country of ref document: EP Kind code of ref document: A1 |
|
| DPE1 | Request for preliminary examination filed after expiration of 19th month from priority date (pct application filed from 20040101) | ||
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 07855624 Country of ref document: EP Kind code of ref document: A1 |