KR20030017459A - Phytomics: a genomic-based approach to herbal compositions - Google Patents

Abstract

The present invention relates to a herb for the purpose of determining which specific biological activity a particular component of the herbal composition can respond to, predicting the biological composition of a particular herbal composition, determining the relevance of the herbal composition, and developing improved herbal therapies. Provide the tools and methodologies necessary to guide the standardization of the composition. The present invention provides tools and methodologies for creating, maintaining, improving and utilizing a hub bioreaction array (HBR array) that constitutes a data set associated with a particular herbal composition.

The HBR array of the present invention contains a gene expression profile and also contains information about plant-related parameters of the herb components, marker information collected after exposing the biosystem to the herb composition, and biological information collected after exposing the biosystem to the herb composition. It may also include reaction information.

Description

Phytomics: A GENOMIC-BASED APPROACH TO HERBAL COMPOSITIONS

All publications and patent applications herein are incorporated by reference as if each individual publication or patent application were specifically and individually indicated and incorporated by reference.

Herbal medicines have been used for centuries by Asians and Europeans. In the United States, herbs have been commercially recognized in the dietary supplement industry as well as in holistic medicine. About one third of the US population has been taken at least once in the form of alternative medicine (Eisenberg et al., 1993, N. Engl. J. Med. 328: 246-252).

Herbal plants have also been the focus of interest for the identification of new active agents for treating diseases. Active compounds derived from plant extracts continue to be of interest in the pharmaceutical industry. Taxol, for example, is an anticancer drug obtained from the bark of the western yew tree. It is estimated that about 50% of the numerous drugs currently commonly used and prescribed are derived from plant sources or contain chemical mimics of plant compounds (Mindell, ER, 1992, Earl Mindell's Herb Bible , A Fireside Book).

In recent years, many pharmaceuticals, food supplements, dietary supplements, etc. contain herbal ingredients or herbal extracts. Herbal medicines have been used for many years to treat various diseases in humans and animals in many other countries (for example, IA Ross, 1999, Medicinal Plants of the World, Chemical Constituents, Traditional and Modern Medicial Uses , Humana Press ; D.Molony, 1998, The American Association of Oriental Medicine's Complete Guide to Chinese Herbal Medicine, Berkley Books; Kessler et al., 1996, The Doctor's Complete Guide to Healing Medicines , Berkley Health / Reference Books; Mindell, supra See).

Herbal medicines. There are many branches of herbal medicine all over the world, such as Ayurveda, Unani, Sida, and Traditional Chinese Medicine (TCM). Modern Western medicine typically consists of administering a single chemical that can intervene a specific biochemical pathway, while each prescription of TCM is typically designed to interact in a manner that harmonizes with multiple targets in the body. Contains hundreds of chemicals from herbs. Although empirical practice has contributed to this ancient herbal medicine's important composition of herbal compositions and prescriptions, they also differ in degree by a set of theories that distinguish them from modern western medicine in anatomy, pharmacology, pathology, diagnostic treatment, etc. Supported. In the field of herbal medicine, TCM has been well documented and carried out by local doctors to cope with a huge population (more than 1.3 billion) in East Asia and China, including Korea and Japan, and a more complete set of theories over the centuries. Has been developed.

Western medicine generally uses purified compounds directed towards a single physiological target, ie natural or synthetic compounds. However, the compositions used in TCM usually consist of a number of herbs and compounds that target multiple targets of the body based on unique and holistic concepts. TCM primarily uses crude natural products with various compounds and agents to treat other forms with little side effects. The greatest possibility of TCM has not yet been realized for most people in the world.

In a typical TCM regimen, the herb serves as a secondary herb that includes a principal herb and supplements, adjuvant and intraocular herbs. The drug has a major effect in treating the cause or major symptoms of the disease. Adjuvant herbs help to intensify the effects of the main drug and exhibit major efficacy in the treatment of the accompanying symptoms. There are three types of adjuvant herbs: 1) to enhance the therapeutic effect of the main and adjuvant herbs or to treat tertiary symptoms, 2) to reduce or eliminate the toxicity and other side effects of the main and adjuvant herbs, and 3) acting on complementary target tissues that are not clearly affected by the main contract. Intraocular herbs direct the effects of other herbs on the affected area and / or modulate and mediate the effects of other herbs in a prescription or formulation. Unlike most herbal drugs or supplements, which consist of one or more parts of a single plant, the intended effects of TCM are directed at multiple tissues.

For example, the known TCM prescription "Ephedra Decoction" used to treat asthma consists of ephedra, broiler branches, bitter apricot heart and licorice. Ephedra, the main medicine, repels colds, induces sweating, and promotes the flow of qi in the lungs, alleviating the major symptoms of asthma. As an adjuvant herb, the broiler branch increases the sweating of ephedra and warms the channel to ensure the flow of the Yang phase, reducing headaches and pantalgia. Bitter apricot sim as an adjuvant herb promotes reverse flow of the lungs and enhances asthma relief by ephedra. Licorice, as an ocular herb, moderates the effects of both ephedra and broilers to ensure the homeostasis of the Vital phase. Each of the four herbs obviously exhibits their respective activities, while they complement and complement each other when they are mixed. Indeed, medications may be prescribed with one or more secondary herbs, depending on the symptoms of the patient's examination (Prescription of Traditional Chinese Medicine, Chapter One, pp10-16, E.Zhang, editor in Chief, Publishing House, Shanghai University of Traditional Chinese Medicine, 1998).

The main theories of TCM, which lead the treatment of diseases with other means, such as herbal medicine and acupuncture, are 1) Yin and Yang theory, 2) the theory of Five Elements, 3) tenon theory, and 4) Qi. , Blood, body fluid theory, and 5) channel and collaterals theory.

In TCM, the first important point in making a proper diagnosis is to determine whether the disease is negative or positive. For example, if a patient with fever is thirsty, constipated, or has a fast pulse, it is positive. Cold patients do not thirst, diarrhea and slow pulse is a negative characteristic. In addition, the properties, tastes and functions of herbs can be classified according to Yin Yang theory. For example, cold and cool natural herbs belong to Yin, while natural warm and hot herbs belong to Yang. A sour, bitter and salty herb belongs to yin, while a hot, sweet and mellow herb belongs to yin, and a herb with astringent and sedimentary functions is yin, while dispersing, rising and floating Herbs with function belong to sheep. In TCM, the principle of treatment is based on the preponderance and inferiority of yin and yang. Herbs are prescribed according to their yin yang characteristics and their ability to restore yin yang imbalance. In doing so, the benefits of treatment are achieved.

According to the five-element theory, there are five basic substances that make up the material world (ie wood, fire, earth, metal and water). In TCM, the theory is used to explain the physiology and pathology of the human body and to guide clinical diagnosis and treatment. Traditional Chinese doctors have solved many effective and specific treatment regimens by applying the rules of creation, restriction, subordination, and inverse restriction of the five elements, for example by reinforcing soil to produce metal ( Benefiting the lungs, growing trees to nourish the kidneys (nourishing the kidneys to benefit the liver), supporting the soil to limit the trees (supplementing the spleen's function to heal excess activity), And strengthening water to control fire (supplementing the core of the kidneys to heal cardiac hyperactivity). Specifically, some hub characteristics are assigned to each of the five elements for the purpose of guiding the TCM prescription.

In TCM, the internal organs of the human body are divided into three groups: the five organs (heart, liver, spleen, lungs and kidneys), the flesh (gallbladder, stomach, large intestine, small intestines, bladder and three seconds), and the extraordinary organs (brain) , Bone marrow, bones, blood vessels, gallbladder and uterus). In TCM, Viscera or Bowel is not only an anatomical unit but also a concept of physiology and pathology of interactions between different organs. For example, the heart is also involved in mental function and affects the function of blood, hair, tongue and skin. Yin-Yang and five-element theory influence the interactions between these intestines, parts and organs. The complexity of the interaction of the theories is used to explain the pathology of the disease in which herbs are prescribed, as discussed below.

Prescription of herbal medications in TCM begins with a diagnosis consisting of four main items: questionnaire, examination, auscultation and retraction, pulsation and palpation. During the questionnaire, a lot of information is collected, including the characteristics of the main symptoms. For example, if the main symptom is characterized by lukewarm pain in the upper abdomen that can be alleviated by warming and pressing, this suggests a lack of spleen-yang. Inability to withstand cold or pain in the lower back and knees, or cold extremities, is evidence of kidney-positive weakness. During the examination, the vitality, skin color and overall appearance and the condition of the tongue are observed. For example, a pale complexion essentially indicates that the lungs have declined and their groups have dried. This occurs when there is a lack of positive air and the circulation of air and blood is disturbed or when a cold is caused that causes the runway and the runway to contract.

In TCM, energy from the intestines, blood and body fluids comes from the intestines, parts, tracks and corridors, tissues and organs to perform their physiological functions; It depends on the formation and metabolism of qi, blood and body fluids.

TCM believes that roads, subpaths and their dependent parts are distributed throughout the body. Through them, herbs affect pathological targets and improve disease. For example, ephedra acts on the pathways of the lungs and bladder to sweat to improve asthma and promote diuresis. As noted above, the application of acupuncture to the clinic is also driven by the theory of driveways and bypasses.

In summary, the nature or characteristics of each herb in TCM are divided into yin, yang, and five circles, but they act through the runways and sub-paths and allow the body to produce therapeutic effects on targets such as intestines and blood. And through body fluids. Pathological factors are presumed to be attracted through the very same system of mileage and bypass, resulting in adverse effects on intestinal and wealth effects, causing illness.

From the foregoing discussion it is clear that TCM terms are as philosophical as there are anatomical concepts. For example, the heart represents many tissues, organs or systems of the body that contribute to the functions described in TCM. Thus, the concept of heart requires a multidimensional data set that allows each concept of TCM to be described. If this is achieved, a molecular holistic medicine can be developed.

US Regulatory Process. In the United States, dietary supplements (e.g., plant products, vitamins, minerals, amino acids and tissue extracts) are regulated under the Dietary Supplement Health and Education Act of 1994 (the DSHE Act). have. The law removed the components of dietary supplements from regulations as food additives under the Federal Food, Medicine and Cosmetics Act. In addition, the DSHE Act requires the Food and Drug Administration (FDA) to be responsible for demonstrating that dietary supplements traded represent a serious or unreasonable risk under the conditions of use on the label or during routine consumption. Thus, there is no recent federal regulation that establishes specific standards for the purification, identification and manufacturing of dietary supplements. In addition, few publications have been made on the quality of herbs resulting from the establishment of the Alternative Medicine Bureau by the National Assembly in 1992 (Angell et al., 1998, N. Engl . J. Med . 339: 839-841).

Currently, FDA must approve each of the chemicals in the pharmaceutical composition or cocktail and then clinical trials must be conducted to obtain separate FDA approval to sell the drug. This process is extremely boring and expensive. Molecular holistic medicine requires slightly less difficult evaluations since previous use of certain herbal compositions as food drugs initially allowed clinical trials with a number of chemicals (ie, certain components of herbal compositions or herbal compositions). Clinical trial using). Recently, FDA has approved testing of several herbal medicines as food drugs in clinical trials (FDA Guidance on Botanical Drugs, April, 1997). While the event generally represents positive progress towards health care, it also reveals important debates about the formulation, manufacture and quality control of herbal and dietary supplements, including traditional Chinese medicine.

Numerous related biological reactions caused by a number of chemicals in herbs are of little use in recent years and will be increasingly important to support marketing approval by the FDA.

Hub-based industries have faced increased pressures to improve the quality of current practice (eg, Angell et al., Supra ). The need to apply scientific tests for the preparation and administration of herbal medicines and food supplements has been highlighted by recent reports of toxicity resulting from the consumption of herbal based formulations. For example, one patient who received herbal-based dietary supplements experienced digitalis toxicity (Slifman et al., 1998. N. Engl. J. Med. 339: 806-811). Herbal ingredients labeled as plantain in the supplement were contaminated with Digistalis lanata , an herb that is actually known to contain at least 60 cardiac glycosides. In another example, herbal preparations have been found to cause chronic lead poisoning in patients (Beigel et al., 1998. N. Engl. J. Med. 339: 827-830). This is not a completely unexpected event because lead and other heavy metal contamination is well known for traditional Asian herbal prescriptions (Woolf et al., 1994, Ann. Intern. Med. 121: 729-735).

Characterize plant medicines. Genetic identity (eg genus, species, cultivar, variety, clone), number of years of herb growth, harvest time, specific plant part used, processing method, geographic origin, soil type, climate type, form and proportion of fertilizer , And other growth factors affect a particular chemical composition of a particular herb “harvested” from a particular area.

Increasing the number of tests in various forms, including numerous chemical analyzes as well as tests at the macro and micro level, have been undertaken to ensure the consistent quality of herbs used in medicine and dietary supplements. Recently, high performance liquid chromatography (HPLC) profiles of marker molecules in herbal extracts have become one of the reference standards. However, this approach has the problem that some of the bioactive molecules are unable to absorb UV or visible light for HPLC detection and the amount of chemicals is not necessarily proportional to its biological potential. For this reason, herbal preparation mixes raw herbs from different sources to minimize chemical denaturation.

Mass spectrometry (MS) is an analytical method for determining the relative mass and relative abundance of components by the light of ionized molecules or molecular fragments produced from a sample under high vacuum. Unlike HPLC, MS is not light dependent. In practice it is used in conjunction with HPLC or capillary electrophoresis (CE): HPLC can separate chemicals and then MS can be used to identify what they are. Commercial systems can be used that are integrated MS and HPLC for biological use. MS is limited to samples that can be gaseous at low pressure or can be so by volatilization or derivatization.

The steps are no longer appropriate. Recent publications report greater fluctuations in the quality of herbs by specific suppliers and the difficulty of providing the bioequivalence of herbal extracts. Moreover, the interrelationship between safety and efficacy and chemicals at the hub are not well defined in most cases. Recently in response to complaints from consumer groups and management agencies (Federal Register, February 6, 1997.Volume 62, No. 25, Docket No. 96M-0417, cGMP in Manufacturing, Packing or Holding Dietary Supplements, Proposed Rules), Herb manufacturers have begun to meet the Pharmaceutical Manufacturing Quality Control Standards (GMP), which require stringent controls at all levels.

Chemical and spectroscopic methods are used to characterize the components of herbal and food supplements. For example, three new headerase-based acetylated saponins were isolated from the fruit of Gliricidia sepium using these two methods (Kojima et al., 1998, Phytochemistry 48 (5): 885-888). In many commercial samples, the plant source of Chinese herbal drugs is deduced by comparing the contents of any characteristic component analyzed by HPLC or CE (Shuenn-Jyi Sheu, 1997, Journal of Food and Drug Analysis 5 (4): 285). -294). For example, the ratio of ephedrine / pseudoephedrine is used as a marker to distinguish Ephedra intermedia from other species; The total alkaloid content is used to distinguish Phellodendron species; The content of gingenosides is used to distinguish Panax species. However, these methods do not directly measure the effects of various herbs on the molecular, physiological or morphological responses of using herbs in human treatment.

Using Gas Chromatography-Mass Spectrometry and Atomic Absorption, the California Department of Health, Food and Drug Bureau recently tested Asian medicines from herbal stores for contaminants (RJKo, 1998, N. Engl.J. Med. 339: 847). Of the 260 products they tested, at least 83 (32%) were contaminated with undeclared drugs or heavy metals, and 23 had more than one mixture. HPLC, gas chromatography and mass spectrometry have found that a mixture of eight commercially available herbs (PC-SPES) contains estrogen organic compounds (DiPaola et al., 1998, N. Engl . J. Med. 339: 785-791). The investigators concluded that PC-SPES has strong estrogen activity and prostate cancer patients taking PC-SPES can disrupt the results of standard treatments and experience clinically significant adverse effects. Gas chromatographic data were also collected for other samples of the traditional Chinese drug 'Way Ling Xi', and related to the anti-inflammatory activity of the samples (Wei et al., Study of chemical pattern recognition as applied to quality assessment of the traditional Chinese medicine "wei ling xian," Yao Hsueh Pao 26 (10): 772-772 (1991)). The study failed to provide an appropriate HBR arrangement, such as a control over time, dose dependent response, and discernment of biomarkers, as well as an understandable database for characterizing the effects of herbal compositions by using an iterative form of data construction. It could not be established.

Changes in protein levels are also used to characterize the effects of herbal compositions or certain components of the herb. For example, the production of granulocyte leukocyte stimulating factor (G-CSF) from peripheral blood mononuclear cells was found to change depending on the specific Chinese herb added to the culture (Yamashiki et al., 1992, J. Clin. Lab. Immunol. 37 (2): 83-90). Expression of the interleukin-1 alpha receptor is controlled to be markedly enhanced in cultured human epidermal keratinocytes treated with Sho-saiko-to, the most commonly used herbal medicine in Japan (Matsumoto et al. al., 1997, J pn. J. Pharmacol. 73 (4): 333-336). Expression of Fc gamma 11/111 receptor and complement receptor 3 in macrophages was increased by treatment with Toki-shakuyakusan (TSS) (JCCyong, 1997, Nippon Yakurigaku Zasshi 110 (Suppl. 1): 87-92 ). Tetrandrine, an alkaloid isolated from natural Chinese herbal medicines, inhibits signal-induced NF-kappa B activity in rat alveolar macrophages (Chen et al., 1997, Biochem. Biophys. Res. Commun . 231 (1): 99-102). Sairay-to, Alismatis ryoma (Japanese name "Takusha") and Hoelen (Japanese name "Bukuryou") Herbs Synthesize Endotherin-1 in Mice with Antiglomerular Base Membrane Nephritis And interfered with expression (Hattori et al., 1997, Nippon Jinzo Gakkai Shi 39 (2): 121-128).

In addition, increases or decreases in mRNA levels have been used as indicators of the effects of various herbs and herbal compositions. Intraperitoneal injections of anti-epileptic traditional Chinese medicines, Qingyangshen (QYS) and diphenylhydantoin sodium, resulted in α- and β-tubulin mRNAs and hippocampal c- in kinin-induced chronic actuation in rats. Force mRNA induction was reduced (Guo et al., 1993, J. Tradit.Chin.Med. 13 (4): 281-286; Guo et al., 1995, J.Tradit.Chin.MED. 15 (4) : 292-296; Guo et al., 1996, J. Trat. Chin. MED. 16 (1): 48-51). Expression of Plasminogen Activator Inhibitory Type I (PAI-1) Specific mRNA when Cultured Human Umbilical Vein Endothelial Cells (HUVECs) with Saponin Astragalose IV, a Component Purified from Astragalus membranaceus Decrease and increase tissue-type plasminogen activator (t-PA) specific mRNA (Zhang et al., 1997, J. Vasc. Res. 34 (4): 273-280). One component purified from the Panax ginseng root is a potent inducer of the production of interleukin-8 (IL-8) by human mononuclear leukocytes and human mononuclear leukocyte cell line THP-1, which induces increased IL-8 mRNA expression. It was found to accompany the symptom (Sonoda et al., 1998, Immunopharmacology 38: 287-294).

Recent advances in nucleic acid microarray technology have enabled the massively parallel investigation of information on gene expression. The process has been used to study genetic diseases, including cell cycle, biochemical pathways, genome-wide expression in yeast, cell growth, cell differentiation, cellular response to single compounds and the development and progression of genetic diseases (M. Schena et al., 1998, TIBTECH 16: 301). Because cells respond to changes in the microenvironment caused by changes in the level of expression of a particular gene, the identification of the gene expressed in the cell determines what the cell is from and what biochemical and regulatory systems contain among others. (Brown et al., 1999, Nature gebet., 21 (1) supplement: 33). Thus, the cellular gene expression profile shows the origin, current differentiation of the cell and cellular response to external stimuli. To date, no researchers have attempted to apply these new techniques to study the molecular effects of whole herbal remedies and supplements.

Some researchers have attempted to characterize the effects of major active ingredients isolated from selected herbs. For example, treatment of notoginsenoside R1 (NR1) purified from Panax notoginseng with HUVECs increases TPA synthesis depending on dose and time (Zhang et al., 1994, Arteriosclerosis and Thromobosis 14 (7): 1040-1046). Treatment with NR1 does not alter the urokinase-type plasminogen activator and PAI-1 antigen synthesis and does not affect the deposition of PAI-1 on extracellular matrix. Treatment of HUVECs with NR1 more than doubles TPA mRNA, whereas expression of PAI-1 specific mRNA is not significantly affected by NR1. Because most studies on Panax notogenes include mixtures with other herbs, the researchers found it difficult to assess what would result in vivo when the results were used to treat the human body ( Id. , at 1045, second column, first paragraph). The researchers also studied only one major ingredient in the herb, so it is impossible to confirm the molecular effect of the interaction between the whole herb or its components.

Dobashi et al. Studied the effects of two of the main components of psycho reagents, a Chinese herbal drug used to treat nephropathy, bronchial asthma and chronic rheumatoid arthritis (1995, Neuroscience Letters 197: 235-238). Administration of SS-d increased the levels of adrenocorticotropin (ACTH), proopiomelanocortin mRNA levels in the anterior pituitary gland, and CRF mRNA levels in the hypothalamus of rats with dose. Let's do it. In contrast, administration of SS-a did not affect the levels of these molecular markers. The study showed that administration of SS-d played an important role in psycho reagent-induced CRF release and CRF gene expression in the hypothalamus of mice, while failing to show molecular effects as a whole herb drug.

Kojima et al. Describe the use of differentiation displays of mRNA to isolate and identify genes that are transcriptionally regulated in the liver of mice by Show-Psycho-Sato, an herbal drug used to treat various inflammatory diseases in Japan (1998, Biol. Pharm. Bull. 4: 426-428). The researchers limited their work to using mRNA differentiation display technology to investigate the molecular mechanisms of herbal medicines. The study also failed to show effects in various organs of treated animals and did not provide any guidance on quality control, new uses and standardization of effects. In addition, the study failed to analyze each component of the herb and compare the individual results with the results obtained using the whole herbal mixture.

Marge et al. Examined the therapeutic effect of Astragali Membranasus herb on the retention of sodium and water in rats experiencing aortocaval fistula-induced experimental hemorrhagic heart failure (1998, Chinese Medical Journal 111 (1)). (17-23). Chronic heart breakdown rats treated with astraglia and chronic heart breakdown rats without a variety of morphological features (eg, weight, serum sodium concentrations); Physiological characteristics (eg mean arterial pressure, heart rate, hematocrit, plasma osmolality); mRNA expression levels (e.g., hypothalamic arginine vasopressin (AVP), AVP V ₁ a receptor, renal AVP V ₂ receptor, aquaporin-2 (AXP2)) and protein secretion (e.g., plasma atrial natriuretic peptide ( ANP) and urinary cyclic guanidinylphosphate (cGMP). The researchers found that treatment with astraglia improved heart and kidney function, partially corrected abnormal mRNA expression in the AVP system and AQP2, and improved renal responses to ANP. These studies did not indicate that the collected data were used as a guide in the development of new formulations or that they were used to identify synergistic or interactions between the various herbs in the formulations and did not distinguish between effects for quality control purposes. Could not establish.

As indicated by the review of the relevant scientific papers above, molecular reference techniques are used to explore or confirm cellular or molecular responses in biological systems in which multiple chemicals, such as herbal medicines and TCMs, are simultaneously treated or administered. It wasn't. In addition, these recent advances do not integrate with other technologies and methods for making processes for the systematic exploration of the biological effects of herbal medicines and TCMs.

The present invention relates to herbal compositions. More specifically, the present invention provides means and methodologies for improving the selection, testing, quality control, and manufacturing of herbal compositions, helping to develop new herbal compositions, and identifying new uses of existing herbal compositions.

1 provides a schematic of the steps of a basic method for constructing a standardized herbal bioreaction arrangement (HBR arrangement) for any selected herbal composition. This figure is shown in the most basic form for ease of understanding. As described herein, each path in the schematic can be repeated repeatedly. In addition, the information contained in one box can be used to guide decisions about the information gathered in the other box. In this way, a number of feedback loops are possible throughout the configuration.

FIG. 2 illustrates a basic method for constructing a hub bioreaction array (HBR array) for any batch herbal composition and for comparing this batch HBR array to a selected subset of information from a standardized HBR array. Provides a schematic of the steps. This figure is shown in the most basic form for ease of understanding. As described herein, each path in the schematic can be repeated. In addition, the information contained in one box can be used to guide decisions about the information gathered in the other box. In this way, numerous feedback loops are possible throughout the configuration.

3 provides a schematic diagram of the steps of a basic method for establishing and using a primary data set. This figure is shown in the most basic form for ease of understanding. As described herein, each path in the schematic can be repeated. In addition, the information contained in one box can be used to guide decisions about the information gathered in the other box. In this way, numerous feedback loops are possible throughout the configuration.

4. Weston blots for various herbal compositions.

A. No herbal composition at all.

B. Huang Qing Tang A (HQT A) (0.2 mg / ml).

C. HQT A (4 mg / ml).

D. HQT B (0.2 mg / ml).

E. HQT B (4 mg / ml).

F. Scute (0.2 mg / ml).

G. Scute (4 mg / ml).

HPLC for Paeonie lactiflora pallus .

6. HPLC for Ziziphi fructus .

7 provides a schematic for establishing bioresponse data for a herbal composition. The data set is based on differently expressed genes induced by three or more different concentrations of herbal agents in mammalian cell culture.

8. FIG. 8 provides a schematic for establishing a characteristic expression profile database or HBR arrangement for a herb drug or a combination herb formulation.

9 provides a schematic diagram for identifying an unknown herbal composition. The expression profile caused by the unknown herbal agent is associated with an expression profile database and a statistical method is employed to assess the possible identity of the herbal agents recorded in the database.

10. FIG. 10 provides a schematic diagram for extracting signature genes for herbal compositions or complex herbal formulations.

FIG. 11 provides a schematic for extracting signature genes for individual chemicals in a herb drug or complex herbal preparation.

12. Clustered representation of gene expression data resulting from cells treated with three forms of single urea herb extract (Cordyceps Sinensis Mycelium (CSM), ST024, ST117) with high and low concentrations (represented by H and L, respectively). .

(A) Cluster analysis was performed by program "cluster" (Eisen et al. , 1999) with 492 selected genes (see text).

(B) Magnified image of genes upregulated by ST117 treatment but downregulated by other herbal extract treatments. The clone ID and estimated gene name are shown.

(C) The clustering algorithm split the CSM, ST024 and ST117 into three completely different clusters. As indicated by the hierarchical branching, the distance between each cluster can be regarded as the difference in expression profiles of cells treated with the three herbal extracts.

Figure 13.

(A) Pseudo-color encoded display of cluster results as calculated based on the selected 492 genes. The boxes in (A) indicate the positions of the three clusters of genes described above.

(B) Magnified image of genes downregulated by CSM but upregulated by others.

(C) Genes upregulated by all kinds of herbal treatments.

(D) Genes that are downregulated by CSM and upregulated by others. IMAGE clone ID and putative gene name are indicated.

Figure 2. Two batches of multicomponent herbal formulations of Huang Chin Tang with high and low concentrations (represented by H and L, respectively) (PHY906-303503 (# 11) and PHY906-284003 (# 12)). Clustered representation of expression data from. The data were averaged based on three repeated experiments on three different dates. Cluster analysis was performed based on 500 selected genes (see text). The clustering algorithm split # 11-L, # 11H and (# 12-H and # 12-L) into three completely different clusters. The distance or similarity coefficient between clusters is represented by a hierarchical clustering dendrogram.

15. Magnified image of (A) mean and (B) individual gene expression levels measured by three independent experiments. Box 1 contains genes that are down regulated in # 11-L treated cells and up regulated in others, and Box 2 contains genes that are up regulated by all herbal treatments. Box 3 contains genes that show no response by # 11-L treatment but are down regulated by others. Box 4 contains genes that are highly down-regulated by low-density herbal treatments, but have milder response in high-density herbal treatments. The clone ID and estimated gene name are shown next to each gene.

16. Classification of gene expression profiles in cells treated with herbal medications. (A) hierarchical cluster of data sets normalized with expression data of untreated control cells and (B) standardized to have zero-mean and unit-variance. (C) Non-hierarchical clustering results by self-organizing maps algorithm.

17. Candidate class predictors for the classification of herbal medications based on gene expression profiles induced by herbal medications. Fifty class predictors with expression profiles to distinguish # 11 and # 12 herbal formulations are shown in this figure. The IMAGE clone ID and putative gene name are shown next to each gene.

18. Gene expression profiles induced by batches of five different concentrations of the composite herbal formulation. 6x4 clustering of expression profiles is shown in (A) and detailed descriptions of gene expression profiles for the selected clusters are shown in (B).

FIG. 19 illustrates how the expression profile of FIG. 18 is classified into three different groups for subsequent hamming distance calculations.

20 shows analysis results of gene expression profiles induced by five batches of complex herbal formulations. The numbers in the table are Hamming distances. The smaller the distance, the more similar the expression profile.

FIG. 21 (A) shows a table of integrated peak intensities of four chemical components in HPLC analysis of five batches of complex herbal formulations. The table introduces two additional parameters, BG + B and BG / B, and (B) shows a six-parameter radial plot so that one batch is more than the second batch # 18 than the other batch by HPLC analysis. It is similar.

22. In Jurkat T cells, display of signature genes induced by complex herbal preparation, Huang Chin Tang.

Figure 23 illustrates the principle of identifying signature genes caused by individual chemical components in a mixture of herbal medications. The signature gene is a gene whose expression level correlates with the amount of chemical component in the herbal agent and the correlation coefficient is greater than 0.99 or less than -0.99. (A) shows that the R value between the gene and glycyrrhizin is 0.998, and (B) shows that the R value is -0.997 for genes whose expression level increases with the decrease of wogonin. .

Figure 24. Signature genes induced by the compound component Albiflorin in Huang Chin Tang, a composite herbal preparation in Jurkat T cells. (A) shows a gene that has a positive relationship with albiflorin, and (B) shows a gene that has a negative relationship with albiflorin.

25. Correlation of gene expression profiles for control. (A) is the gene expression profile of the control group and (B) is the gene expression profile of the sample group. (C) shows the number of genes with differential expression rates that are at least twice as large as the concentration of the herbal treatment.

26. Cluster of expression profiles clustered by a non-hierarchical analysis program, where the program is based on the principle of self organization map (SOM). The X-axis represents the herb concentration from low to high and the Y-axis represents the gene-expression ratio.

FIG. 27 shows the induction and suppressor genes commonly found in two batches of Huang Chin Tang.

SOM cluster results for two batches of Huang Chin Tang. (A) shows SOM cluster results for the expression profile of two batches of Huang Chin Tang. (B) shows that ten genes responded similarly to the two batches, and (C) shows how the weight factor decreases as cluster I and cluster j change more.

29. Calculation of S scores between paired herbal formulations in cluster analysis. (A) is a table of scores and (B) shows how relevant the five batches of similar herbal formulations are.

The present invention provides a means and methodology for creating, maintaining, improving and utilizing Herbal BioResponse Arrays (HBR arrays). Wherein the HBR arrangement constitutes a data set associated with a particular herbal composition. The HBR arrangement of the present invention includes information of plant-related variables of herb components, collected marker information following exposure of the biosystem to the herbal composition, and collected biological response information following exposure of the biosystem to the herbal composition. .

The present invention provides the means and methodology necessary to establish a standardized HBR arrangement for a particular herbal composition. The standardized HBR arrangement is used here as a measure for evaluating batches of similar or different herbal compositions. The present invention also provides the means and methodology needed to update and maintain a standardized HBR arrangement. Certain aspects of the present invention include iterative processes in which additional batch data, additional plant related data, additional marker information, and / or additional biological response information of the herbal composition is periodically added to a standardized HBR arrangement. Thus, the present invention provides a means and methodology for creating, maintaining, updating and using HBR arrangements on an ongoing basis.

The present invention leads to standardization of herbal compositions; Determine specific components of the herbal composition that are responsible for the particular biological activity; Predict the biological activity of the herbal composition; Developing improved herbal therapies; Adjusting or altering the herbal composition; Determining the relevance of different herbal compositions; Identifying specific molecules in a batch herbal composition that possess the desired biological activity; Determining a herbal component of the known herbal composition that can be removed from the known herbal composition while maintaining or improving the desired biological activity of the known herbal composition; Herbal ingredients, including identifying previously unknown biological activities and new uses for batch herbal compositions and designing therapies using methods of combinatorial chemistry using the predicted biological activities of batch herbal compositions and It provides the means and methodology necessary to assist in designing therapies including synthetic chemical drugs.

More specifically, the present invention provides a method of establishing a standardized HBR arrangement for a herbal composition, wherein the method

a) selecting a characterized herbal composition;

b) collecting data for two or more markers after exposing the biosystem to the characterized batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring the gene expression pattern of the cells from the cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step of selecting as a marker a gene at the expression level that has changed due to exposure to the herbal composition;

c) storing the marker data of step b) as a standardized HBR arrangement.

In addition, the invention further includes a method of exposing the biosystem to one or more batch herbal compositions, collecting data for one or more biological responses, and adding the collected biological response data to a standardized HBR arrangement for the herbal composition. to provide.

The present invention provides a method of evaluating a herbal composition, wherein the method exposes a biosystem to a batch herbal composition, collects data for two or more markers, and collects collected marker data the same as that of the batch herbal composition. Or comparing to a standardized HBR arrangement for a substantially identical herbal composition.

The present invention provides a system for predicting the biological activity of a herbal composition, which system is

1) a biosystem comprising one or more other types of cells, tissues, organs or in vitro analytes;

2) batch herbal compositions;

3) two or more molecular markers

4) means for exposing the biosystem to the batch herbal composition and measuring specific responses of the molecular markers;

5) a computer processor including a memory for analyzing and storing specific response measurements of the molecular markers to create an HBR arrangement for the batch hub composition;

6) A computer processor comprising a memory for predicting the biological activity of the batch hub composition by comparing the HBR array of the batch herbal composition to one or more previously stored HBR arrays, wherein the computer processor comprises a memory to generate the one or more previously stored HBR arrays. The biological activity of the herbal compositions to be used is known.

All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described below.

Overview of the Invention

As described above, the present invention relates to means and methods useful for predicting biological responses of herbal compositions. More specifically, the present invention provides methods for using HDB array databases as well as methods for using HDB array databases to improve the design of effective herbal based therapies. It is an object of the present invention to design, create, improve and use an overall HBR arrangement for the preparation, testing and administration of herbal compositions and also to lead to the development of new herbal compositions and new uses of existing herbal compositions.

Phytomics. As used herein, "phytomics", depending on the context in which it is used, refers to the use of bioinformation and statistical approaches to represent qualitative and quantitative aspects of herbal composition components and to indicate such aspects. Represents a real database that has been developed.

Hub Bioreaction Array (HBR Array). As used herein, an HBR arrangement constitutes a data set of two or more observations or surveys associated with the herbal composition. HBR arrays are biometrics obtained after exposure of the biosystem to the herbal composition and marker information obtained after exposure of the biosystem to the herbal composition, including qualitative and quantitative data (plant related data) for the plant in the composition, dose dependent studies. Contains a database of response data. The data of any particular HBR array can be statistically analyzed in two or three dimensions.

HBR arrays can be represented as batch HBR arrays and standardized HBR arrays. The batch HBR array is a data array associated with a particular batch herbal composition. The standardized HBR arrangement is an arrangement of data associated with the standardized herbal composition.

Key data set. As used herein, the term "major data set" refers to a data set that serves as a data set baseline for comparing or otherwise analyzing various other data sets for the same or different herbal compositions. In general, a major data set is made using biotechnological techniques to identify genetic or protein aspects of herbal compositions. Thus, the main data set is usually but not always based on data sets of genomes and proteins. For example, the nucleic acid microarray result may be a major data set used to compare different, dependent, or few data sets.

A small or dependent data set. As used herein, a "minor data set" or "dependent data set" refers to one or more data sets that are used to compare to the primary data set. In general, but not always, a minority data set will consist of information about the herbal composition collected by more traditional methods. For example, a minority or dependent data set consists of a set of plant related data obtained by more traditional means. Examples of plant related data include, but are not limited to, the genus / species of the herb (s) of the herbal composition, the specific plant portion of the herb (s) of the herbal composition, and the geographic location where the herbal (s) were located. Another example of a minority data set consists of a series of biological responses of cells, tissues, organs or individuals after treating the herbal composition in one or more different amounts. Examples of such biological data or whole organisms include, but are not limited to, cytotoxicity studies, enzyme treatment studies, growth rates, weight gain or loss, changes in motor skills, and changes in mental power.

Herb. Technically speaking, the herb is a small, non-tree (ie fleshy stem), an annual or perennial seed plant in which all growing parts of the air dry at the end of each growing season. Herbs are valuable because of their medicinal, flavorful or fragrant quality. As used herein and as used more generally, "herb" refers to a plant or plant part having the use of food supplements, drugs, medicines, treatments or life extension. Thus, as used herein, a herb is not limited to the botanical definition of the herb, but rather includes any plant or plant part of a multicellular plant species or subspecies, including herbs, shrubs, sub-trees and trees. It is a botanical, plant or plant part taken from a plant used for that purpose. Plant parts used in herbal compositions include, but are not limited to, seeds, leaves, stems, branches, branches, eyes, flowers, scaly stems, round stems, tubers, rhizomes, stems, roots, berries, conifers ), Berries, forming layer and bark.

Herbal composition. As used herein, "herb composition" refers to any composition comprising a herb, herb plant or herb plant part. Thus, as used herein, herbal compositions are all herbal formulations, including herbal food supplements, herbal medications, herbal medications, and medicinal foods. Examples of herbal compositions include but are not limited to the following ingredients; Whole plants or plant parts of a single plant species; Multiple components derived from the entire plant, plant parts, or single plant species of multiple plant species; Multiple components derived from multiple plant species; Or combinations of the various components. For a thorough review of the various herbal compositions, see, for example, Kichang, The Palmaology of Chinese Herbs, Seed Press (1993), which is incorporated herein in its entirety. Representative examples of various herbal compositions are provided in the following paragraphs.

Herbal compositions comprising the bark of willow have been used in the UK to treat heat since the mid-18th century. The active ingredient of the willow bark is a bitter glycoside called salincin, which is hydrolyzed to produce glucose and salicylic alcohol. Aspirin (acetylsalicylic acid), aspirin-based drugs (such as ibuprofen), and all drugs often referred to as non-steroidal anti-inflammatory drugs (NSAIDs) are often used to treat pain, fever and inflammation. Meadowsweet is another herb that contains salicylate. Treating arthritis or arthritis-like symptoms with willow bark or meadowsweet requires a large consumption of herbal tea made from the plant. The entire Poplar species (ie poplar and shrub) also contain salicylate precursors and poplar eyes have been used as anti-inflammatory, antipyretic and analgesic agents.

Herbal compositions that are used to treat a variety of diseases and other health related problems affecting humans and animals have been published as US patents. For example, US Pat. No. 5,417,979 discloses compositions comprising herbal mixtures including Stephania and Glycyrrhiza species, as well as their extracts, which are used as appetite stimulants and pain medications. Herbal compositions comprising Glycyrrhiza uralensis have been found to be useful for treating eczema, psoriasis, itching and inflammatory reactions of the skin (US Pat. No. 5,466,452). U.S. Pat.No. 5,595,743 discloses licorice extract ( Glycyrrhiza ), sigesbeckia, sophora, stemona and stemona and tet used in the treatment of various mammalian diseases including inflammation and rheumatoid arthritis. Various herbal compositions are disclosed, including tetrandra. Inflammation of the eye can be treated with a pharmaceutical composition containing the vegetable alkaloid Tedrandrin (US Pat. No. 5,627,195).

U.S. Patent No. 5,683,697 discloses discloses a pharmaceutical composition having anti-inflammatory, antipyretic, anti-cough effect the composition Melia (Melia), anje Picard (Angepica), dendeuro byum (Dendrobium), lymphatic tienseu (Impatiens), Citrus Plant parts from Citrus , Loranthus , Celosia , Cynanchun and Glehnia species. Alpinia (Alphinia), seumil Rocks (Smilax), Tinos Fora (Tinospora), tree bulruseu (Tribulus), above Tania (Withania), and the roots of the rare version (Zingiber), the root hub comprising an extract of the stem and / or vegetation The composition has been found to reduce or attenuate the symptoms associated with rheumatoid arthritis, osteoarthritis, reactive arthritis and reduced production of proinflammatory cytokines (US Pat. No. 5,683,698).

Herbal compositions may include capsules, tablets, coated tablets, pellets, extracts or tinctures, powders, fresh or dried pastes or pastes, prepared teas, juices, creams and ointments, essence oils or combinations of these forms. Available in form. Herbal medications are administered by any one of several methods, including orally, rectally, parenterally, into the digestive tract, percutaneously, and intravenously and locally via feeding tube. Herbal compositions encompassed by the present invention include herbal compositions that also include non-hub components. Such non-herbal components include all or part of insects, molluscs, animal or insect droppings, natural oils or oils, ammonium carbonate, tartarate, alcohol, water, glycerin, steroids, medicines, vitamins, nutrient extracts, whey, Salts and gelatins, including but not limited to.

For oral administration, the disclosed herbal compositions include, for example, binders (e.g., pregelatinized corn starch, polyvinylpyrrolidone or hydroxypropylmethylcellulose), fillers (e.g., lactose, microcrystalline cellulose or calcium phosphate). , Lubricants (e.g. magnesium stearate, talc or silica), disintegrants (e.g. potato starch or sodium starch glycolate) or wetting agents (e.g. sodium lauryl sulfate), glidants, artificial and natural flavors And in the form of tablets or capsules prepared by conventional methods by mixing with generally acceptable excipients such as sweeteners, artificial and natural pigments and dyes and solubilizers. The herbal compositions can be further formulated to release the active substance in a time-release manner, as known in the art and discussed in US Pat. Nos. 4,690,825 and 5,055,300. Tablets may be coated by methods known in the art.

Liquid preparations for oral administration may be prepared, for example, in the form of solutions, syrups, suspensions or slurries (such as liquid supplements described in US Pat. No. 5,108,767 to Mulchandai et al., 1992) or prior to use. It can be prepared as a dry product for reconstitution with water or other suitable vehicle. Liquid formulations of folic acid and other vitamins and minerals will be in the form of liquid nutritional supplements specifically designed for ESRD patients. Such liquid preparations include suspending agents (eg sorbitol syrup, methyl cellulose or hydrogenated edible fats), emulsifiers (eg lecithin or acacia), non-aqueous vehicles (eg almond oil, oily esters or ethyl alcohol); It may be prepared by conventional methods together with pharmaceutically acceptable additives such as preservatives (eg methyl or propyl p-hydroxybenzoate or sorbic acid) and artificial or natural colorants and / or sweeteners.

For topical administration, the herbal composition may contain a carrier, diluent or excipient that is acceptable to provide a composition such as a cream, gel, solid, paste, ointment, powder, lotion, liquid, aerosol medicine, etc. that is most suitable for topical administration. It is combined by mixing with other constituents. Sterile distilled water alone and simple creams, ointments and gel bases can be used as carriers of the herbal compositions. Preservatives and buffers may also be added. The formulations may be applied to sterile dressings, biodegradable and easily absorbable patches or bandages or slow release implantation systems where high initial release results in slower release.

For a more complete overview and discussion of herbal based compositions, see Earl Mindell, Earl Mindell's Herb Bible, Simon & Schuster (1992); Culpeper's Complete Herbal (W.Foulsham & Co., Ltd., first published in the mid 1600's) and Rodale's Illustrated Encyclopedia of Herbs , Rodale Press ( 1987).

Standardized herbal composition. As used herein, a "standardized herbal composition" or "characterized herbal composition" is selected as a standard herbal composition to evaluate a batch herbal composition having components that are the same, similar, or different from those of the standardized herbal composition. It refers to the specific herbal composition to be made. It is also sometimes referred to herein as the "master herbal composition." Standardized herbal compositions are generally herbal compositions that well characterize and describe the desired biological response in a particular biosystem. Standardized herbal compositions are usually standardized by chemical tests known to those of ordinary skill in the art and are stored appropriately for long time use and reference. Standardized herbal compositions are used to establish a standardized HBR arrangement to characterize herbal compositions based on observations and measurements on plants (ie plant related data), markers, and bioreactions.

Batch herbal composition.

As used herein, “batch herbal composition” refers to any test herbal composition that is used to establish an HBR arrangement based on observations and measurements on plants and markers to characterize the herbal composition. Also referred to herein as "test" or "batch" herbal compositions. Observation and measurement of biological responses may or may not be included. The herbal composition used to establish a standardized herbal composition is also referred to as a "batch herbal composition" until specified as a "standardized herbal composition".

Batch. As used herein, “batch” is a particular amount of herbal composition that can be identified to distinguish it from any other specific amount of the same herbal composition in terms of certain properties. For example, one batch of herbal composition is distinguished from another batch of the same herbal composition in which one of the batches is harvested at a different time or from a different geographic location. Other differences that distinguish a particular batch include, but are not limited to, the following: 1) The specific plant part to be used (e.g., the root of the herb is used in one batch while the leaves of the same herb are used in another batch). Used); 2) post-harvest treatment of each herb and herb composition (eg, one batch is treated with distilled water while the other batch is treated with hydrogen chloride to mimic acid in human stomach); And 3) the relative proportions of each hub in the herbal composition (eg, one batch is the same weight or volume part as three other hubs, while the other batch is more proportionally more different than the other two).

Bio system. As used herein, a "biosystem" is a biological entity for observing or measuring a biological response. Thus, biosystems include, but are not limited to, cells, tissues, organs, whole organisms or ex vivo analytes.

Biological activity. As used herein, the "biological activity" of an herb refers to the specific biological effects specific to the herb composition in a given biosystem.

Plant-related data. As used herein, "plant-related data" refers to data collected for a herb composition that includes, but is not limited to, plants, growth conditions and data relating to the harvesting process and handling of post-harvest plants. The plant-related data also includes the relative proportions of the components in the herbal composition, each of which may be a different plant part, another plant species, another non-vegetable ingredient (eg insect part, chemical agent) or a combination of these variables. .

The plant-related data collected for the herbal composition is not limited but includes: 1) plant species (if available, specific plant varieties, varieties, clones, families, etc.) and the specific plant parts used in the composition; 2) geographic origin of the hub including longitude / latitude and altitude; 3) the use and cultivation of pesticides, including fertilizer types and quantities, rainfall and seasons and irrigation amounts and seasons, average microEinsteins, herbicides, insecticides, acaricides and fungicides per day; 4) methods and conditions used in the treatment of herbs, including the age / growth of the herbs, soaking time, drying time, extraction method and milling method; and 5) storage methods and conditions of the herbal components and the final herbal composition. .

In addition, standardized herbal compositions can be analyzed chemically. Chemical characterization can be performed by any chemical analytical method generally known to those skilled in the art. Examples of applicable chemical assays include, but are not limited to, HPLC, TCL, chemical pinkerprint, mass spectrometry, and gas chromatography.

Cell banking system. As used herein, a "cell banking system" includes a Master Cell Bank (MCB) and a Working Cell Bank (WCB) of cells. The use of cell banking systems reduces variation for herbal drug testing and is used for all cell types in nucleic acid microarray studies.

Bioinformatics. As used herein, "bioinformatics" refers to the use and organization of information of biological interest. Bioinformatics includes, among others, (1) data acquisition and analysis; (2) database development; (3) integration and connection; And (4) further analysis of the database obtained. Almost all bioinformatics resources have been developed as shared freeware until the early 1990s and are still available free of charge over the Internet. Some companies have developed their own database or analysis software.

Genome or genomics. As used herein, "genomics" refers to studies of genes and their functions. Genomics emphasizes the integration of basic and applied research in comparative gene mapping, molecular cloning, large scale restriction mapping and DNA sequencing and computer analysis. Genetic information is obtained using basic techniques such as DNA sequencing, protein sequencing and PCR.

Gene function is achieved by (1) analyzing the effects of DNA mutations on the genes of cells, tissues, organs or organisms whose development and health are normal; (2) by analyzing various signals encoded in the gene sequence; (3) It is measured by studying a protein produced by a gene or related gene system.

Proteomics or proteomics. As used herein, "proteomics", also called "proteome research" or "phenome," refers to quantitative protein expression patterns of the genome under defined conditions. As commonly used, proteomics refers to high throughput automated assays using protein biochemistry.

In addition to genomic research, many prerequisites are needed to conduct protein research. First, gene expression levels do not necessarily indicate the amount of active protein in a cell. In addition, the gene sequence does not exhibit post-translational modifications necessary for the function and activity of the protein. Moreover, the genome itself does not exhibit a dynamic cellular process that alters protein levels up or down.

The protein program attempts to characterize all proteins in the cell, identifying at least the amino acid sequence of the isolated protein. Generally, proteins are first isolated using 2D gels or HPLC, and then peptides or proteins are sequenced using a high throughput mass spectrometer. Using a computer, the mass spectrometer's output can be analyzed to link the gene with the specific protein it encodes. The whole process is sometimes referred to as "functional genomics." Numerous commercial ventures now offer proteomic services (eg Pharmaceutical Proteomics ™, The Protein Chip ™ System from Seafer Biosystems; Perceptive Biosystems).

For general information on protein research, see eg Yale University Press, 1999. s. Proteins, Enzymes , Jeans of Fruton: JS Fruton, 1999, Proteins, Enzymes, Genes: The Interplay of Chemistry and Biology , Yale Univ. Pr,); Proteome Research , Wilkins et al., Springer Barrack: Wilkins et al., 1997, Proteome Research: New Frontiers in Functional Genomics (Principles and Practice) , Springer Verlag; 1999 by Humaner PI. second. 2-D Protom Analytical Protocols (Method in Molecular Biology) , 112 (AJLink, 1999, 2-D Proteome Analysis Protocals (Methods in Molecular Biology) , 112, Humana Pr.); See Kamp et al., 1999, Proteome and Protein Analysis , Springer Verlag, et al., 1999, Camper Barrack's Camp.

Signal Transduction. As used herein, "signal transduction", also known as cellular signal transduction, refers to the entire pathway by which a cell receives an external signal, transmits it, amplifies it, and directs it. Signaling pathways require exchange chains of proteins that sequentially carry signals. Since many signal transductions involve receiving extracellular chemical signals, protein kinases often participate in this cascade of chain reactions that cause phosphorylation of cytoplasmic proteins to amplify the signal.

Post-detoxification deformation. As used herein, “post-translational modification” is a generic term used to encompass alterations that occur in a protein after the protein has been synthesized as a primary polypeptide. Such post-translational modifications include but are not limited to glycosylation, removal of N-terminal methionine (or N-formyl methionyl), single peptide removal, acetylation, formylation, amino acid modification, release of smaller proteins or peptides. Internal cleavage of the peptide chains, phosphorylation and modification of methionine for

Array or microarray. As used herein, "array" or "microarray" refers to a lattice system with each position or probe cell occupied by a defined nucleic acid fragment. The array itself is sometimes referred to as a "chip", "biochip", "DNA chip" or "gene chip". High density nucleic acid microarrays often have thousands of probe cells of various lattice styles.

Once the sequence is created, the DNA or protein molecules derived from the biosystem are added and some chemical action occurs between the DNA or protein molecules and the sequence, giving any recognition pattern unique to the sequence and biosystem. Radioisotope-labeled sets of radiographs are available for other options, but are traditional detection strategies that include fluorescence, colorimetric analysis, and electronic signal transduction.

Markers. As used herein, the term "marker" refers to a biological measurement or observation of a particular herbal composition that characterizes a particular biosystem that has been exposed to a particular batch of herbal composition. The term "marker" includes both qualitative and quantitative measurements and observations of a biosystem. The marker database constitutes a data set that characterizes gene expression patterns in response to herbal treatment, wherein the gene expression patterns indicate which genes begin, end, face up, or face down in response to a specific herbal composition. Thus, a "marker" refers to a biological measurement or observation of an up and down or transient control of expression levels or a qualitative or quantitative change in a biosystem that is used to characterize the different biological responses of a biosystem of a herbal composition.

The particular batch of herbal composition to which the biosystem has been exposed can be an unknown herbal composition or a known or standardized herbal composition. Examples of markers useful in carrying out the invention include, but are not limited to, molecular markers, cytogenetic markers, biochemical markers or macromolecular markers. Macromolecular markers include, but are not limited to, enzymes, polypeptides, peptides, sugars, antibodies, DNA, RNA, proteins (both translated and post-translational proteins), nucleic acids, polysaccharides.

Markers that meet the definition of "marker" herein are suitable for carrying out the present invention. The term "marker" includes related and replaceable terms such as "biomarker", "genetic marker" or "gene marker." Along with secondary markers, one or more primary markers or hierarchical markers may be used to increase the discernment capability of the HBR array. The molecular markers thus selected allow for more accurate and extended HBR arrangements in combination with various other molecular, genetic, biochemical or macromolecular markers.

Molecular markers include one or more microscopic molecules from one or more molecular compounds, such as DNA, RNA, cDNA, nucleic acid fragments, proteins, protein fragments, lipids, fatty acids, carbohydrates and glycoproteins.

The establishment, generation and use of applicable molecular markers are known to those of ordinary skill in the art. Examples of particularly useful techniques for the characterization of molecular markers include differential display, reverse transcriptase polymerase chain reaction (RT-PCR), large-scale sequencing of expression sequence labels (ESTs), serial analysis of gene expression (SAGE), Western immunonoble 2D, 3D studies and microarray techniques of lots or proteins. Those skilled in the art of molecular marker technology are familiar with the methods and uses of such techniques (Bernard R. Glick and Jack J. Pastornak, Molecular Biotechnology, Principles and Applications of Recombinant DNA, Second Edition , ASM Press) (1998). ); Mathew R. Walker and Ralph Rapley, Route Maps in Gene Technology , Blackwell Science) (1997); Roe et al., DNA Isolations and Sequencing , John Wiley & Sons (1996); James D. Watson et al., Recombinant DNA, Second Edition , Scientific American Books (1992).

Methods of isolating and sequencing DNA, RNA and proteins are known to those skilled in the art. Examples of such known techniques include Molecular Cloning: A Laboratory Manual 2nd Edition , Sambrook et al., Cold Spring Hobor, NY (1989); Berkhauser's Han Spit Sauls and Jay. blood. Hanspeter Saluz and JPJost, A Laboratory Guide to Genomic Sequencing: The Direct Sequencing of Native Uncloned DNA (Jonst's Gene Sequencing) Biomethods Vol 1) , Birkhauser (1998); And Willie's Rain. DNA isolation and sequencing (B. Roe et al., DNA Isolation and Sequencing , Wiley) (1996). Examples of traditional molecular biology techniques include in vitro ligation, restriction endonuclease digestion, PCR, cellular transformation, hybridization, electrophoresis, DNA sequencing. , Cell culture, and the like. Certain commercially available kits and means for use in the present invention include RNA isolation, PCR cDNA library construction, retroviral expression libraries, vectors, gene expression assays, protein antibody identity, cytotoxicity assays, protein expression and purification, and high efficiency plasmid purification. ( Clontechnics Products Catalog, XIII (3), 1-32 (1998) or www.clontech.com; Atlas ™ Products Catalog (1998); Sigma (See Product Catalog 1997).

For discussion, methodologies and applications of oligonucleotide arrays, microarrays, DNA chips or biochips are described, for example, in US Pat. Nos. 5,445,934, 5,605,662, 5,631,134, 5,736,257, 5,741,644, 5,744,305. , 5,795,714; Parallel human genome analysis by Szenna et al .: monitoring microarray-based expression of 1000 genes, Proc. Natl. Acad. Sci. USA 93, 10614-10619 (Schena et al., Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes, Proc. Natl. Acad. Sci. USA 93, 10614-10619) (1996); Quest for something metabolic and genetic regulation of gene expression on a genomic scale, such as the City of Derry, Science 278, 680-686 (DeRisi et al., Exploring the Metabolic and Genetic Contral of GeneExpression on a Genomic Scale, Science 278, 680 -686) (1997); Genome, such as Saccharomyces MRS three Levy cyano right dikka at-wide expression monitoring, Nature Biotechnology 15, 1359-1367 (Wodicka , et al, Genome-wide Expression Monitoring in Saccharomyces cerevisiae, Nature Biotechnology 15, 1359-1367.) ( 1997); Complete genome expression monitoring of the Parque de: The Human Race, Nature Biotechnology 15, 1343-1344 (Pardee , Complete Genome Expression Monitoring: The Human Race, Nature Biotechnology 15, 1343-1344) (1997); DNA variations of Scarper et al., Nature Biotechnology 16, 33-39 (Schafer et al., DNA Variation and the Future of Human Genetics, Nature Biotechnology 16, 33-39) (1998); Pick up during such use of the cDNA micro arrays for analyzing gene expression patterns in human cancer, Nature Biotechnology 14, 457-460 (DeRisi et al., Use of a cDNA Microarray to Analyze Gene Expression Patterns in Human Cancer, Nature Genetics 14 457-460 (1996); Detection and analysis of inflammatory disease related genes using cDNA microarrays such as Heller et al . , Proc. Natl. Acad. Sci. USA 94, 2150-2155 (Heller et al., Discovery and Analysis of inflammatory Disease-Related Genes Using cDNA Microarrays, Proc. Natl. Acad. Sci. USA 94, 2150-2155) (1997); Marshall et al., Nature Biotechnology 16, 27-31 (Marshall et al., DNA Chips: An Array of Possibilities, Nature Biotechnology 16, 27-31) (1998); Schenna et al .: Microb Discovery Platform for Functional Genomics, Tibtech 16, 301-306 (Schena et al., Microarrays: Biotechnology's Discovery Platform for Functional Genomics, Tibtech 16, 301-306) (1998); Ramsay's DNA Chips: State of Technology, Nature Biotechnology 16, 40-44 (Ramsay, DNA Chips: State-of-the-art, Nature Biotechnology 16, 40-44) (1998); The genetic information access, Science 274, 610-614 (. Chee et al, Acessing Genetic Information with High-Density DNA Arrays, Science 274, 610-614) (1996) having a high-density DNA arrays, such value; Prof. Prof. et al., Prof. Isolation of differentially expressed genes by cDNA microarray system with colorimetric detection, et al., Genomics 50, 1-12 (Chen et al., Profiling Expression Patterns and Isolation Differentially Expressed Genes by cDNA Microarray System with Colorimetry Detection, Genomics 50, 1-12) (1998); blood. Characterization of the stress-inducing effect of homocysteine, such as Andrew Outinen, Biochem. Genetics such as J. 332, 213-221 (P. Andrew Outinen et al., Characterization of the stress-inducing effects of homocysteine, Biochem. J. 332, 213-221) (1998), and Gelbert, have actually For more information, see Curr Opin Biotechnol 8 (6), 669-674 (Gelbert et al., Will genetics really revolutionize the drug discovery process, Curr Opin Biotechnol 8 (6), 669-674) (1997).

Other more specific references that may be used in the present invention include, but are not limited to, ESTs (see Michael R. Fannon, Gene expression in normal and disease states-identification of therapeutic targets, TIBTECH 14, 294-298 (1996)); Generation of protein profiles (see Robinson et al., A Tyrosine Kinase Profile of Prostate Carcinoma, Proc. Natl. Acad. Sci. USA 93, 5958-5962 (1996)); Chemical and spectroscopic methods for identifying components of herbal compositions (Kojima et al., Saponins from Gliricidia sepium, Phytochemistry 48 (5), 885-888 (1998)); Determination of functional antigens (see Aris Persidis, Functional antigenics, Nature Biotechnology 16, 305-307 (1998)); HPLC (see Milton TW Hearn (Editor), HPLC of Proteins, Pepties, and Polynucleotides: Contemporary Topics and Application (Analytical Techniques in Clinical Chemistry and Laboratory Manual) , VCH Pub. (1991)); Electrophoresis (Westermeier et al., Electrophoresis in Practice: A Guide to Methods and Applications of DNA and Protein Separation , John Wiley & Sons (1997)) and cross-reaction marker analysis (Irving Millman et al., Woodchuck Hepatitis Virus: Experimental) Expression techniques such as Infection and Natural Occurrence, Hepatology 4 (5): 817-823 (1984)). The use of structural genomics to describe the structure of all proteins decoded in the finished genome (where the methodology includes high-efficiency direct structural determination and computer-assisted methods) is the use of structural genomics in Terry Gassterland: Bioinformatics in Driver Sheets. (Terry Gaasterland, Structure genomics: Bioinformatics in the driver's seat, Nature Biotechnology 16, 625-627). Regarding the methodology of bioinformatics, published by John Willy & Sons, Andreas Baxevanis (editor), Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins , John Wiley & Sons (1998). See Luke Alphey, DNA sequencing: From Experimental Methods to Bioinformatics (Introduction to Biotechniques Series) , Springer Verlag (1997).

Cytogenetic variables include, but are not limited to, karyotype analysis (eg, relative chromosome length, centromere location, presence or absence of secondary stenosis), karyotyping (ie, a graphical representation of an organism's karyotype), mitosis and meiosis Behavior, Chromosome Staining and Plasma Forms, DNA-Protein Interactions (known as Nuclease Hydrolase Protection Assays), Neutron Scattering Studies, Rolling Circles (AM Diegelman and ETKool, Nucleic Acids Res 26 (13): 3235-3241 (1998); Backert et al., Mol. Cell. Biol. 16 (11): 6285-6294 (1996); Skaliter et al., J. Viol. 70 (2): 1132-1136 (1996); A Fire and SQXu, Proc . Natl . Acad . Sci. USA 92 (10): 4641-4645 (1995)) and radionuclear imaging of the whole nucleus followed by incubation with radiolabeled ribonucleotides.

Biochemical variables include, but are not limited to, analysis of specific pathways such as signal transduction, protein synthesis and transport, RNA transcription, cholesterol synthesis and degradation, glucose production, and glycolysis.

Fingerprint method. As used herein, the term "fingerprint method" refers to a means of creating a characteristic profile of a substance, particularly a hub, to identify it. As used herein, the "fingerprint method" refers to the display of the results of a particular means applied to the fingerprint method. Examples of various fingerprinting methods include, but are not limited to, DNA fingerprinting, protein fingerprinting, chemical fingerprinting, and footprinting.

DNA fingerprinting or profiling refers to a method of creating unique patterns from DNA of a particular biological source (eg, a particular plant, plant species, genus, plant part or plant tissue). DNA fingerprints or profiles can be used to distinguish one biological source from another. Patterns obtained by analyzing batches using microarrays, oligonucleotide sequences, DNA chips or biochips are also referred to as "fingerprints."

Protein fingerprinting refers to the generation of a pattern of proteins in an organism such as a cell, tissue, organ or plant, which provides a fully characterized "fingerprint" of that cell, tissue, organ or organism at that time.

Chemical fingerprinting refers to the analysis of low molecular weight chemicals in cells, and the resulting patterns are used to identify organisms such as cells, tissues, organs or plants. The analysis is generally performed using gas chromatography (GC), HPLC or mass spectrometry.

The footprint method is a way of finding how two molecules stick together. In the case of DNA, proteins are attached to labeled DNA, which then breaks down by an enzyme or chemical attack. The process produces a "ladder" of fragments of all sizes. If DNA is protected by bound proteins, the degradation is less and the ladder weakens. Footprinting is a common technique for automatically tracking proteins that regulate gene activity that actually binds to DNA.

The means or methods used to perform each type of fingerprinting method are described in detail elsewhere in this specification.

BioResponses. As used herein, "bioreaction" refers to the observation or measurement of a biological response of a biosystem following exposure to a herbal composition. It is also sometimes referred to herein as a "biological effect." Bioreactions are quantitative or qualitative data points for the biological activity of certain herbal compositions. Bioresponse data include both dose and transient information, which is known to those of ordinary skill in the art of measuring the response of the biosystem to various treatments. Thus, bioresponse data includes specific biological response information of a particular biosystem for specific doses of the herbal composition administered in a particular manner over a particular time period.

Biological responses include, but are not limited to, physiological, morphological, cognitive, attracting, autonomic, and post-translational modifications, such as signal transduction measurements. Many herbal compositions show more than one bioreaction (see Kee Chang Hung, The Pharmacology of Chinese Herbs , CRC Press (1993)). Some specific bioreactions may be included in one or more groups of the groups described above, or may have aspects or components of the reaction that include one or more groups. Biological responses that can be applied to the present invention are known to those of ordinary skill in the art. The following references illustrate the state of the art in this area: The Pharmacology of Chinese Herbs , CRC Press (1993); Earl Mindell's Herb Bible , Simon & Schuster (1992); Goodman and Gilman, The Pharmacological Basis of Therapeutics, Ninth Edition , Joel G. Hardman et al. (Eds.), McGraw Hill, Health Professions Division (1996); Elements of pharmacology by P. J. Bentley , A primer on drug action , Cambridge University Press (1981); P.T.Marshal and G.M. Hughes' Physiology of mammals and other vertebrates, Second Edition , Cambridge Unversity Press (1980); Report of the Committee on Infectious Diseases , American Academy of Pediatrics (1991); Knut Schmidt-Niels's Animal Physiology: Adaptation and Environment, 5th Edition , Cambridge University Press (1997); Elaine Yen. Human Anatomy & Physiology of Marib, Addison-Wessley Pub. Co. (1997); William F. Gannog, Review of Medical Physiology (18th Ed) , Appleton & Lange (1997); Arthur. Guyton and John Lee. Hall's Textbook of Medical Physiology, WBSaunders Co. (1995).

"Physiological response" refers to the physiology or function of a biosystem. Physiological responses to cells, tissues, or organs include, but are not limited to, temperature, blood flow rate, pulse rate, oxygen concentration, bioelectrical potential, pH value, cholesterol levels, state of infection (e.g., viruses, bacteria), and ion flux. . Physiological reactions to the whole organism include: gastrointestinal tract actions (e.g. ulcers, upset stomach, indigestion, heartburn), genital actions (e.g. physiologically based genitals, uterine cramps, dysmenorrhea), and excretory actions (e.g. urinary tract disorders, kidney disease, diarrhea) , Constipation), blood circulation (eg, high blood pressure, heart disease); Oxygen consumption, skeletal health (eg osteoporosis), conditions of cartilage and connective tissue (eg joint pain and inflammation), exercise, vision (eg myopia, blindness); Muscle tone (e.g. wasting syndrome, muscle tone), presence or absence of pain, epidermal and skin health (e.g. skin irritation, itching, skin wounds), endocrine system function, cardiac function, nerve cooperation, head related health (e.g. : Headache, dizziness), age (eg life span, longevity) and breathing (eg congestion, breathing related diseases).

A "morphological response" is one that exhibits properties related to the morphology or shape and structure of a biosystem after exposure to a herbal composition. Morphological responses, regardless of biosystem type, are size, mass, height, width, color, degree of inflammation, overall appearance (e.g. opacity, transparency, paleness), degree of wetting and drying, presence or absence of cancer growth, and parasites. Or the presence or absence of pests. Morphological responses to the whole organism base include the amount and location of hair growth (eg hustism, baldness), the presence or absence of wrinkles, the shape and extent of nails and skin growth, the extent of blot clotting, the presence of inflammation or wounds Or the presence or absence of presence and hemorrhoids.

"Cognitive response" refers to a characteristic related to the cognitive or mental state of the biosystem after exposure to the herbal composition. Cognitive responses include, but are not limited to, perception, discernment, ideation, judgment, memory, reason and imagination.

An "attractive response" refers to a characteristic related to the motivation or attraction of the biosystem after exposure to the herbal composition. Attractant responses include, but are not limited to, emotions (feeling good), desires, learned drivers, certain physiological needs (eg, appetite, sexual drivers) or similar stimuli that act as stimuli for action (eg, stamina, sex drivers) Do not.

"Autonomous reaction" refers to the characteristics associated with the autonomous response of the biosystem after exposure to the herbal composition. Autonomic reactions are related to the autonomic nervous system of biosystems. Examples of autonomic reactions include, but are not limited to, involuntary actions (eg, nervousness, panic attacks), or physiological needs (eg, breathing, heart rhythm, hormone release, immune response, insomnia, narcolepsy).

Bioreactions of cells, tissues, organs and whole organisms treated with various herbal compositions or herbal components are well known in the herbal arts. For example, the herb compositions Sairay-To (TJ-114), Alismatis ryoma (Japanese name 'Takusha') and Hoerene (Japanese name "Bukuryo") have been described for the synthesis and synthesis of endoterin-1 in rats. It is known to inhibit expression, respectively (Hatorie et al., Sairay- to, may inhibit the synthesis of endoterin- 1 in renal glomeruli, Nippon Jinzo Gakkai Shi 39 (2), 121-128 (1997)). Interleukin (IL) -1 alpha production was markedly enhanced by treatment of cultured human epidermal keratinocytes with the herb drug Sho-Psycho-Toe (Matsumoto et al., Self-secreting growth of cultured human keratinocytes by Show-Psycho-Toe) Enhancement of Interleukin-1-alpha, Jpn J. Pharmacol 73 (4), 333-336 (1997). Addition of Sho-Psycho-Sato to the culture of peripheral blood mononuclear cells from healthy volunteers showed a dose-dependent increase in the production of granulocyte leukocyte stimulating factor (G-CSF) (Yamashiki et al. Psycho-Sat "induces the production of ex vivo granulocyte leukocyte stimulating factors on peripheral blood mononuclear cells, J Clin Lab Immunol 37 (2), 83-90 (1992). We concluded that the administration of show-psycho-sato may be useful for the treatment of chronic liver disease, malignant disease and acute infectious disease in which G-CSF is effective. Human umbilical vein endothelial cells (HUVECs) were treated with saponin astragalose IV (AS-IV) purified from the Chinese herb Astragalus membranaceus and then plasminogen activator inhibitory type 1 (PAI-1) ) -Specific mRNA expression was decreased and tissue-type plasminogen activator (t-PA) -specific mRNA was increased (regulation of fibrinolytic potential of cultured human umbilical vein endothelial cells: intestinal, etc .: Astragallo Side IV down-regulates plasminogen activator inhibition-1 and up-regulates tissue-type plasminogen activator expression, J Vasc Res 34 (4), 273-280 (1997)). One of four components isolated from the Panax ginseng root is a potent inducer of IL-8 production by human monocytes and THP-1 cells, and this induction involves increased IL-8 mRNA expression. (Sonda et al., Stimulation of interleukin-8 production by acidic polysaccharides from the roots of Panax ginseng, Immunopharmacology 38 (3), 287-294 (1998)). Flow cytometry analysis revealed that expression of Fc gamma 11/111 receptor and complement receptors on macrophages was increased by treatment with the campo-hub drug Toki-shakuyakusan (TSS) (Cyong, New BRM from kampo-herbal medicine). Nippon Yakurigaku Zasshi 110 Suppl 1, 87P-92P (1997)). Image analysis for intercellular adhesion molecule-1 expression in MRI / 1pr mice: effects of Chinese herb medicine, Chung Hua I Hsueh Tsa Chih 75 (4), 204-206 (1995). The distribution intensity of hepatic adhesion molecule-1 (ICAM-1), immunoglobulin and C3 was found to be markedly reduced in MRL / lpr mice after treatment with Chinese herb stragulin. Western blot analysis showed that tetradrine, isolated from natural Chinese herbal medicines, inhibited signal-induced NF-kappa B activity in rat afferent macrophages (Chen et al., Tetrandrine inhibits signal-induced NF-). kappa B activation in rat alveolar macrophages, Biochem Biophys Res Commun 231 (1), 99-102 (1997).

algorithm. As used herein, "algorithm" refers to a stepwise problem solving procedure, in particular a computer processing procedure established and repeated with a limited number of steps. Appropriate algorithms for two- and three-dimensional analysis of plant-related, marker and bioreaction data sets are known to those skilled in computer related art. Such algorithms are useful for building the hub bioreaction arrays of the present invention. General information about the algorithm is described, for example, in Jerrod H. Zar, Bioststisical Analysis, second edition , Prentice Hall (1984); Robert A. Schowengerdt, Techniques for image processing and classification in remote sensing , Academic Press (1983); Steven Gold et al., New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence, Pattern Recognition , 31 (8): 1019-1031 (1998); Berc Rustem, Algorithms for Nonlinear Programming and Multiple-Objective Decisions , Wiley-Interscience Series in Systems and Optimization, John Wiley & Sons (1998); Jeffrey H. Kingston, Algorithms and Data Structures: Design, Correctness, Analysis, International Computer Science Series , Addison-Wesley Pub. Co. (1997); See Steven S. Skiena, The Algorithm Design Manual , Springer Verlag (1997) and Marcel F. Neuts, Algorithms Probability: A Collection of Problems (Stochastic Modeling) , Chapman & Hall (1995). For more information on applying algorithms to gene-based data, see, for example, Dan Gusfield, Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology , Cambridge Unversity Press (1997); Melanie Mitchell, An Introduction to Genetic Algorithms (Complex Adaptive Systems) , MIT Press (1996); David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wessley Pub. Co, (1989); Zbigniew Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs , Springer Verlag (1996); Andre G. Uitterlinden and Jan Vijg, Two-Dimensional DNA Typing: A Paralled Approach to Genome Analysis , Ellis Horwood Series in Molecular Biology, Ellis Horwood Ltd. (1994) and Pierre Baldi and Soren Brunak, Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning , MIT Press (1998).

Combinatorial Chemistry. As used herein, "combination chemistry" refers to a number of techniques used to produce hundreds to thousands of chemical compounds, where each compound differs in characteristics such as their form, charge and / or hydrophobicity, and the like. Combination chemistry can be used to produce compounds that are chemical modifications of herbs or herb components. Such compounds can be measured using the methods of the present invention.

The concepts of basic binding chemistry are known to those of ordinary skill in the chemical arts, and also described by Nicholas K. Terrett, Combinatorial Chemistry (Oxford Chemistry, Masters) , Oxford Univ. Press (1998); Anthony W. Czarnik and Sheila Hobs Dewitt (Editors), A Practical Guide to Combinational Chemistry , Amer. Chenmical Society (1997); Stephen R. Wilson (Editor) and Anthony W. Czarnik (Contributor), Combinatorial Chemistry: Synthesis and Application , John Wiley & Sons (1997); Eric M. Gordon and James F. Kerwin (Editors), Combinatorial Chemistry and Molecular Diversity in Drug Discovery , Wiley-Liss (1998); Shmuel Cabilly (Editor), Combinatorial Peptide Library Protocols (Methods in Molecular Biology) , Human Press (1997); John P. Devlin, High Throughput Screening , Marcel Dekker (1998); Larry Gold and Joseph Alper, Keeping pace with genomics through combinatorial chemistry, Nature Biotechnology 15, 297 (1997); Aris Persidis, Combinatorial chemistry, Nature Biotechnology 16, 691-693 (1998).

Example 1 Establishment of Standardized HBR Arrangements for Selected Herbal Compositions.

A basic diagram for establishing a standardized HBR arrangement is provided in FIG. 1. The definition of each component in the schematic is defined above.

After selecting the herbal composition of interest, data is collected for various properties related to the herbal composition. The data includes, but is not limited to, plant related properties and marker information and BioResponse information.

Plant-related data includes plant species, specific plant parts, geographic origin of the plant in the herb composition, growth conditions of the plant, treatment methods used to prepare the herb components, storage methods and conditions, and various chemical analyzes of the herb composition. It is not limited to this. Marker information includes qualitative and quantitative data for markers collected after exposing the biosystem to the herb composite. Applicable markers include, but are not limited to, molecular markers, cytogenetic markers, biochemical markers, and macromolecular markers. Bioreaction information includes qualitative and quantitative data on biological responses collected after exposing the biosystem to the herbal composition.

One or more analyzes of the same, similar, substantially similar, or different batches of the herbal composition of interest can be used to obtain each type of data (eg, chemical, marker, bioreaction). . Such other analysis can be done at the same time or at different times. In addition, data may be collected for the same or different markers at the same time or at different times. Similarly, biometric data can be collected for the same or different biological responses at the same time or at different times. Thus, data collection for the HBR arrays may be done simultaneously or on an on-going basis. When data is collected by exposing the biosystem to the herbal composition, the information on the dosage of the herbal composition is recorded as well as the treatment time. Bioreaction data can also consist of post-translational modifications, such as measurements of signal transduction.

After collecting two or more types of data (e.g., two or more markers and one bioreaction; data on plant-related properties and data on bioreactions), the algorithm can be analyzed to analyze the data. Create dimensional and / or three-dimensional Hub BioResponse Arrays.

Various statistical parameters may be calculated for the HBR array or may be part of the HBR array data set. These statistical parameters may include, but are not limited to, averages, standard deviations, correlation matrices or matched (or mismatched) matrices, ratios, regression coefficients, and transformations (eg, arcsine percent transformation of raw data). It doesn't happen. Thus, the HBR array may consist of raw data and also some calculations, distributions, graphical representations and other data manipulations associated with the raw data. Specific examples of such information include, but are not limited to, large gene expression profiles for digital images, scatter graphs, cluster analysis, and marker data.

Accumulated total data and the resulting analysis constitute a standardized HBR arrangement for the particular herbal composition used to establish the HBR configuration data set. Due to the repetition of the methods used to establish and maintain the HBR arrangement for the herbal composition, such arrangements can be static at any one place in time or dynamically seen over time.

The resulting assay can identify a subset of standardized HBR arrangements that correlate (ie, show general trends) or are correlated (positive or negative) with one or more specific biological activities of a particular herbal composition.

Example 2. Establishment of a batch HBR arrangement for the batch herbal composition.

A basic diagram for establishing the HBR arrangement for the placement of the herbal composition is provided in FIG. 2. The definition of each component in the schematic is defined above. The process of establishing such an arrangement is as described immediately above for a standardized HBR arrangement.

In general, the amount of data collected for a batch HBR array will be less than that collected to establish a standardized HBR array. However, data collected about batch herbal compositions can also be used to add to an established HBR arrangement or to establish a new standardized HBR arrangement.

In general, only data collected about batch herbal compositions is data known to be highly correlated or associated with the desired biological activity of the herbal composition being tested. For example, if it is determined that a particular subset of plant-related data and marker data (based on the standardized HBR sequence data and analysis described above) is highly correlated to the desired biological activity of a particular herbal composition, the batch may be It is only necessary to test the batch herb composition with respect to the properties of the subset to determine whether or not it has activity. By comparing the data obtained on the properties of the subset obtained from the batch (ie, batch HBR arrangement) with the standardized HBR arrangement for a particular herbal composition, one of ordinary skill in the art can determine whether the particular batch has or does not have the desired biological activity. have.

Example 3. Establishment and Use of Primary Data Set.

A basic diagram for establishing and using the main data set for herbal compositions is provided in FIG. 3. The definition of each component in the schematic is defined above.

The first step is the establishment of a main data set relating to the selected herbal composition or batch herbal composition. This is accomplished by exposing the biosystem to the herbal composition and collecting the resulting marker information, which marker information will constitute the main data set. Most of the time, the main data set will consist of genomics and / or proteomics data in the form of arrays such as those obtained with DNA biochips.

Next, the main data set is analyzed to confirm that differential expression / results are obtained for the tested herbal composition. Differential expressions / results are needed to make meaningful algorithms in the next step. Examples of such differential expressions / results include, but are not limited to, an indication in which a gene is upregulated or downregulated in response to exposure to a herbal composition or an indication in which a protein level is increased or decreased in response to exposure.

If no meaningful or useful differential expression / results are obtained, then it is necessary to repeat the exposure and marker collection steps. If you think that the experimental error leads to insufficient results at first, then you can repeat the exposure / data collection step with all the same variables as the first time (eg, same biosystem, same marker set, same experimental protocol). have. However, it may be necessary to change the biosystem sampling (eg, the type of cells used, the stage of cell growth), use different marker sets and / or change the experimental protocols to obtain differential expression / results. .

Example 4 Use of HBR Array Information

The HBR configuration information described herein can be used for many other purposes, including, but not limited to: 1) evaluating the components of the herbal composition; 2) predicting the bioreaction of the herbal composition; 3) determining whether the marker information is highly correlated with the specific bioreaction of the herbal composition; 3) determining which data sets of information (ie, plant related data, marker data and bioreaction data) have a high correlation with the specific bioreaction of the herbal composition; 4) determining which type of biosystem is best for evaluating the biological activity of the herbal composition; 5) adjusting or altering the components of the herbal composition such that the HBR arrangement of the herbal composition corresponds to a standardized HBR arrangement of the same or substantially the same herbal composition; 6) adjusting or altering the components of the herbal composition such that the herbal composition has the desired biological activity; 7) determining association with other herbal compositions; 8) create and update a standardized HBR array; 9) identifying specific components (eg, plant parts, proteins, molecules) that maintain the desired biological activity of the herbal composition; 10) determining which components of the herbal composition can be removed simultaneously with maintaining or enhancing the desired biological activity of the herbal composition; 11) identifying one or more previously unknown biological activities relating to the herbal composition; 12) assisting in the design of therapies comprising herbs and ingredients other than herbs, such as chemically synthesized drugs or pharmaceuticals; And 13) using HBR array information to complement combinatorial chemistry methods for designing therapies. Each embodiment of the present invention may be accomplished by those skilled in the art using the methods and means provided herein.

Example 5 Quality Control.

Using the HBR arrangement technology of the present invention, correlating or determining the standardized, or master, of the same or substantially similar herbal compositions, the substantial equivalents of a particular batch of herbal compositions to the batch (prescription of a single herb or multiple herbs) do. The HBR array used in the present method comprises an overall score consisting of the permissible range of quantitative changes for each biological effect (ie bioreaction) and, if possible, the weights assigned to each biological effect. It may also consist of markers from multiple biochemical pathways of the biosystem.

"Data mining" refers to the method used to determine or select which subset of biological effects is the minimum number of biological effects required in a particular HBR configuration. Data excavation information arises as a result of exposing the biosystem (eg, cell line) to a standardized herbal composition in a dose dependent manner to establish a standardized HBR arrangement. This standardized HBR arrangement can then be compared to the various HBR arrangements established for the test hub composition. These test herbal compositions can be prepared in different batches prepared on different dates; Other batches made from raw herbs collected at different times; And other batches made from native herbs collected elsewhere.

Example 6 Enhancement of Herbal Compositions or Identification of New Uses of Herbal Compositions.

After exposing the biosystem to extracts from individual herbs of the formulation, or extracts from the entire formulation, HBR arrays are generated by examining the biological effects of the extract. The observed biological effects may be from multiple biochemical pathways of the biosystem and / or from multiple tissues of the animal, where various markers are evaluated for their corresponding qualitative and / or quantitative alterations. The resulting HBR arrangement can be compared with a novel HBR arrangement or similar HBR arrangements from other herbal compositions or herbal compositions prepared by other methods. This process is useful for selecting the minimum number of markers needed to predict a given set of biological effects and a given batch herbal composition for a given set of biological effects.

To construct an HBR array, those skilled in the art utilize a variety of data discovery techniques, including, but not limited to, database analysis for statistical analysis, artificial intelligence, and neural work. Statistical selection methods include basic exploratory data analysis (EDA), graphical EDA (bushing, etc.), and multivariate exploratory techniques (e.g. cluster analysis, discriminable factor analyses). ), Cascading linear or nonlinear degeneration, classification tree) (see, for example, STATISTICA ™, software packages from StatSoft, Tulsa, OK 74104; Tel: 918-749-1119; Fax: 918-). 749-2217; www.statsoft.com.

Data exploration tools are used to explore large amounts of HBR array data to construct HBR arrays and uniform patterns within, among, or among various HBR arrays. The process consists of exploration, construction and validation of the HBR array. This process is typically repeated repeatedly until a robustly formed HBR arrangement, or a standardized HBR arrangement, is identified.

Example 7. Establishment of a Standardized HBR Array for Ginseng Prescriptions.

For the purposes of this example, Panax Ginseng C.A. Select with Meyer G115. The growing climate is between -10 and + 10 ° C, with annual rainfall of 50-100 cm (see Huang in The Pharmacology of Chinese Herbs , (1993) pp 21-45, CRC Press, Boca Raton, FL, for reference only) To be fully incorporated). Ginseng batches may first be of geographical origin, species, plant parts (eg, rhizomes, roots, leaf husks, seeds, buds and flowers); Growth conditions, treatment methods and storage conditions before and after treatment. Validation of chemical content for these batches includes TLC qualitative analysis of lipophilic components, including ginsenoside saponins (e.g., Ro, Ra1, Ra2, Rb1, Rb2, Rb3, Rc, For determination of Rg1, Rg2, Rd, Re, Rf, Rh1, Rh2, NG-R2 and Z-R1) will be done by HPLC crystallization analysis (Elkin et al ., Chumg Kuo Yao Li Hsueh Pao (1993) 14: 97-100 and Yoshikawa et al ., Yakugaku Zasshi (1993) 113; 460-467). The saponin content of the other herbs is between 2.1 and 20.6% (by weight), depending on the species (see Table 1). This data will then be stored, preferably in the memory of the computer processor, for the next operation.

Table 1. Saponin Contents of Different Ginseng Herbs ^*

Bell Total saponin (% by weight) Panax ginseng C.A. Meyer Panax quiquefolius Panax notoginseng and Panax japonica Panax japonica var. major 2.1-4.4% 4.9% 13.6-20.6% 9.34% * Source Huang in The Pharmacology of Chinese Herbs , (1993) page 29, CRC Press, Boca Raton, FL.

Expression biomarkers for standard ginseng include the following: IL-8, IL-2, GM-CSF, NfrB, ICAM-1, interferon gamma, choline acetyl transferase, trk A, nerve growth factor (Kim et al. , Planta Med (1998) 64: 110-115; Sonoda et al., Immunopharmacology (1998) 38: 287-294; Baum et al., Eur J Appl Physiol (1997) 76: 165-169; Iwangawa et al. , Free Radic Biol Med (1998) 24: 1256-1268; Rhind et al, Eur J Appl Physiol (1996) 74:. 348-360). Alternatively, for a wider batch size, Affymetrix's 4000,000 oligonucleotide groups / 1.6 cm ² chips can be used (US Pat. No. 5,556,752). Expression biomarkers for standard ginseng will be prepared by nucleic acid microarray techniques using either photolithography, mechanical microspotting or inkjet applications (see Schena et al., TIBTECH (1998) 16: 301-306) . ). With varying conditions to create multiple microarray sets, the set of selected cells will be contacted with standard ginseng over time. The microarray set will then be analyzed by monitoring expression of the hybridization basis of the biochemical extract through subtraction of steady state mRNA levels from fluorescence intensity at each location on the microarray (Schena et al., Science (1995). 270: 467-470; Schena et al., Proc Natl Acad Sci USA (1996) 93: 10614-10619; Lockhart et al. Nat Biotechnol (1996) 14: 1675-1680; DeRisi et al., Nat Genet (1996) 14: 457-460; Heller et al., Proc Natl Acad Sci USA (1997) 94: 2150-2155). The array data set is then input into an algorithm to generate expression biomarker statistics for standard ginseng. Biochemical biomarkers for standard ginseng include quantitative analysis of the increase in cycloheximide-sensitive [ ³ H] -leucine integration and [ ³ H] -thymidine integration, which is proportional to protein synthesis (See Yamamoto et al., Arzneimittelforschung (1977) 27: 1169-1173). For biochemical biomarkers, various conditions in the presence and absence of [ ³ H] -thymidine (for DNA synthesis) or in the presence and absence of cycloheximide and [ ³ H] -leucine (for protein synthesis) may be used for various times. Bone marrow cells will be contacted with standard ginseng to perform multiple quantitative assays of biochemical biomarkers (ie, BBM sets). The BBM set is then input into an algorithm to produce biochemical biomarker statistics for standard ginseng. Then, for the next operation, statistical data will be stored, preferably in the memory of the computer processor.

The biological response (ie bioreaction) of the biosystem will be determined using both cells and animals. For cells, it contains fibroblasts, macrophages, monocytes, PMNL, LAK cells, B16-F10 melanoma cells, THP-1 cells and hippocampal neurons at concentrations from 0.5 mg / ml to 100 mg / ml. Ginseng batches will be exposed to certain cell types, but not limited to these. For animal treatment, 0.5-100 mg / kg of ginseng herbal extracts will be administered orally by intraperitoneal injection and subcutaneous injection.

To determine the biosystem's biological response to standardized ginseng, human ovarian cancer cells will be inoculated into hairless mice, and as a result, tumor formation will be found that can promote. After tumor formation, the young rats will be treated by co-administration of cis-diamminecichloroplatinum and standard ginseng. The mice will be tested for tumor growth inhibition, increased survival time and low side effects on hematocrit levels and body weight (Nakata et al., Jpn J Cancer Res (1998) 89: 733-740). The assay will be repeated using various concentrations of standard ginseng to determine concentration, variance, and variability for each variable.

The collected data will then be multidimensionally analyzed to generate multivariant normal distribution sets as a means of determining the baseline correlation between biological activity and standard ginseng. Zar, JH, in Biostatistical Analysis, 2nd ed . (1984), pp 328-360, Prentice Hall, Englewood Cliffs, NJ). The second independent measure of the biosystem's biological response to standard ginseng would be the effect of standard ginseng on physical performance during exercise. Mice will be treated with standard ginseng at various concentrations (between 0.5-100 mg / kg / day) for 4 days and then tested for maintenance of elevated plasma free fatty acid levels and glucose levels during exercise at about 70% VO2max ( Wang et al., Planta Med (1998) 64130-133). The generated data will be collected and then subjected to multidimensional analysis to produce a set of various modified normal distributions as a means of determining the baseline correlation between biological activity and standard ginseng (see Zar, JH, in Biostatistical Analysis, 2nd ed). . (1984), pp at 328-360, Prentice Hall, Englewood Cliffs, NJ, the specification, which will fully incorporated by reference). The set of distributions for each bioreaction is then input into the algorithm to generate statistical values of standard ginseng. Then, for the next operation, statistical data will be stored, preferably in the memory of the computer processor.

Each of these steps (ie, chemical analysis, biomarker information generation, and biosystem response determination) is repeated to create a large database of statistical values. These values are compiled and entered into an algorithm to generate two-dimensional and three-dimensional hub response arrays for standard ginseng. Repeatedly, the resulting arrangement (ie, standardized arrangement) shows the highest correlation between composition (including growth conditions), biomarker information, and biological responses to standardized ginseng. By determining two or more known related variables for the composition and biomarker information values through the representation on the HBR array for the test batch, the biological values for the test batch are compared by comparing the test values with the standardized HBR array values for the standardized ginseng. The value according to the reaction can be predicted. The resulting predictions will be used to assess the quality of a given ginseng batch without the need to use the observed biological system's biological response (see Example 2).

Example 8 Evaluation of Ginseng, a Selected Herbal Composition Using a Subset of Variables Correlated with Specific Biological Responses.

To assess the quality of a test batch herbal composition, first of all, the data regarding plant related parameters (eg plant species, plant parts, geographic origin, growth conditions, processing methods and storage conditions) for the herbs in the selected herbal composition are collected. Collect. The selected herbal composition can then be treated to conduct a chemical analysis to determine the chemical content of the herb (Elkin et al., Chumg Kuo Yao Li Hsueh Pao (1993) 14: 97-100 and Yoshikawa et al. , Yakugaku Zasshi (1993) 113: 460-467). Previously obtained ginseng data showed a deep correlation between oxygen consumption during aerobics and the presence of saponin components, especially subsets of Rg1 and Rb1 (Wang et al., Planta Med (1998) 64: 130-133). .

Followed by, but not limited to, fibroblasts, macrophages, monocytes, PMNL, LAK cells, B16-F10 melanoma cells, THP-1 cells and hippocampal neurons at concentrations from 0.5 mg / ml to 100 mg / ml The test batch is exposed to test cells to determine expression biomarker values. mRNA is isolated from exposed cells and processed subsequently to be used as a substrate for monitoring expression of hybridization-based expression of biochemical extracts using microarrays comprising IL-8, IL-2 and interferon gamma cDNA. (Schena et al., Science (1995) 270: 467-470; Schena et al., Proc Natl Acad Sci USA (1996) 93: 10614-10619; Lockhart et al., Nat Biotechnol (1996) 14: 1675-1680 DeRisi et al., Nat Genet (1996) 14: 457-460; Heller et al., Proc Natl Acad Sci USA (1997) 94: 2150-2155). Previously obtained ginseng data showed a deep correlation between oxygen consumption and induction of expression biomarkers IL-8, IL-2 and interferon gamma during aerobics in test cells (Venkatraman et al., Med Sci Sports Exerc ( 1997) 29: 333-344 and Wang et al., Planta Med (1998) 64: 130-133). Regarding biochemical biomarkers, rat bone marrow cells will then be exposed to the test batch and analyzed for [ ³ H] -thymidine incorporation that reflects mitosis. Previously obtained ginseng data showed that Rb1 and Rg1 correlated deeply with DNA synthesis in rat bone marrow cells (Yamamoto et al., Arzneimittelforschung (1978) 28: 2238-2241).

After an iterative analysis, the data from each analysis is entered into an algorithm to generate a test HBR array for the selected herbal composition based on the listed plant related data, including chemical analysis and data related to the subset of biomarkers. . By comparing the test HBR and standardized HBR array variables of standard ginseng for the observation and analysis of the subset, the quality of the test batch will be determined, where in vitro of IL-2, IL-8 and INF gamma mRNA in vitro and demonstration of increased [ ³ H] -thymidine integration in rat bone marrow cells (including data collected on growth conditions, origin and demonstration of saponin Rg1 and Rb1), as shown by standard ginseng It is used to predict the equivalent bioreaction effects of the test batch on. Based on this procedure, one can determine whether the test batch is similar or different from the quality of the standard for a given biological response or biological response of interest.

Example 9 Establishment of Standardized HBR Arrangement for Huang Ling Prescription

For the purposes of this example, the standard Huang Ling (HL) is selected as Coptis chinesis Fransce, a Southwest Asian product, known to those skilled in the art for growth conditions. See Huang in The Pharmacology of Chinese Herbs , (1993). ), pp 69 and 287-288, CRC Press, Boca Raton, FL). Arsenic, berberine, caeruleic acid, columbamine, copsin, coptin, coptiside-I, coptiside-II, copticine, corek Coreximine, epiberberine, ferulic acid, greenlandicine, isocytcin, lumicaerulic acid, magnoflorine, oxybererine, Coptis chinesis France for the measurement of thalifendine, umbellatine, urbenine, warenine, palmatine, jatrorrhizine and colubamine The dried rhizome of was verified for chemical content by quantitative chemical analysis (Zhu M., Chung Yao Tung Pao (1984) 9: 63-64). The alberoid berberine content of other herbs is between 7-9% (by weight). For the next operation, this data will be stored, preferably in the memory of the computer processor.

Expression biomarkers for standard HL include: NfkB; bcl-2 homologue, A1; Zinc finger protein, A20; IL-2 receptors; Cell cycle probes; c-Ki-ras2; Growth control probes and glucocorticoid receptor dependent apoptosis probes (Chi et al., Life Sci (1994) 54: 2099-2107; Yang et al., Naunyn Schmiedebergs Arch Pharmacol (1996) 354: 102-108; Miura et al., Biochem Pharmacol (1997) 53; Chang KS, J Formos Med Assoc (1991) 90: 10-14). Alternatively, for a wider batch size, Affymetrix's 400,000 oligonucleotide groups / 1.6 cm ² chips can be used (US Pat. No. 5,556,752). Expression biomarkers for standard HL will be prepared by microarray techniques as described in Example 1, including analysis and statistical data generation. Biochemical biomarkers for standard HL include increased glucocorticoid receptors and inhibition of alpha-fetoprotein secretion in HepG2 cells exposed to HL (Chi et al., Life Sci (1994) 54 : 2099-2107). BBM sets are generated and analyzed as described in Example 1. These data will then be stored, preferably in the memory of the computer processor, for the next operation.

The biological response of the biosystem will be judged using cells and whole animals. At a concentration of 0.1-100 mg / ml of the selected herbal composition for certain cell types, including but not limited to human HepG2 hepatoma cells, human embryonic carcinoma cells and thymocytes Will expose the batch. For animal treatment, 0.1 mg-2 g / kg of coptic herbal composition (ie HL) will be administered orally by intraperitoneal injection and subcutaneous injection. To determine the biosystem's biological response to standardized HL, human embryonic cancer clones, NT2 / D1, were exposed to various concentrations of standard HL and cells were introduced into cells with neuronal-like cell morphology. Differentiation will be examined (Chang KS, J Formos Med Assoc (1991) 90: 10-14). The assay will be repeated and analyzed as described for ginseng in Example 1. The second independent determination of the biosystem's biological response to standard HL would be the effect of standard HL on diarrhea due to enterotoxigenic Escherichia coli (ETEC). Patients with ongoing diarrhea due to ETEC will be treated with varying concentrations of HL (eg 2 g / kg) and stool volume will be measured (see, eg, Rabbani GH, DanMed Bull (1996) 43: 173-185). The assay will be repeated and assayed as described for ginseng in Example 1. The set of distributions for each biological system is then entered into the algorithm to generate statistics for the standard HL. The statistical data will then be stored, preferably in the memory of the computer processor, for the next operation.

Finally, as in Example 1, the steps will be repeated to generate an HBR sequence for the normalized HL, and then the resulting HBR sequence will be used to predict biological activity and assess the quality of the batch. Using this method, a standardized HBR array can be created and updated periodically.

Example 10 Evaluation of Selected Herbal Compositions of Huang Ling Using a Subset of Variables Correlating to Specific Biological Responses.

In order to assess the quality of selected test batches of Huang Ling's herbal composition, first of all, the data is collected in relation to plant-specific properties (eg plant species, plant parts, geographical origin, growth conditions, treatment methods and storage conditions). do. The herbal composition is then processed to allow chemical analysis to determine the chemical content of the composition (Zhu M., Chung Yao Tung Pao (1984) 9: 63-64).

Previously obtained HL data indicated that terminal differentiation of human embryonic cancer clones into neuronal cells correlated strongly with the presence of berberine (Chang KS, J Formos Med Assoc (1991) 90: 10-14). The test batch is then exposed to test cells comprising certain concentrations of human embryonic cancer clones, NT2 / D1, starting at non-toxic concentrations, the determination of concentration being within the skill of one of skill in the art. mRNA was isolated from exposed cells and subsequently processed to be used as a substrate for monitoring expression of hybridization-based expression of biochemical extracts using microarrays comprising IL-2 receptors and NfkB (Chi et al. ., Life Sci (1994) 54 : 2099-2107; Yang et al, Naunyn Schmiedebergs Arch Pharmacol (1996) 354:.. 102-108; Miura et al, Biochem Pharmacol (1997) 53; Chang KS, J Formos Med Assoc (1991) 90: 10-14; US Pat. No. 5,556,752), and use it to determine down regulation of c-Ki-ras2 gene expression in these cells. Previously obtained HL data indicated that terminal differentiation of human embryonic cancer clones into neuronal cells correlated strongly with induction of mitogen probes and down regulation of c-Ki-ras2 gene expression (see: Chang KS, J Formos Med Assoc (1991) 90: 10-14).

For biochemical markers, after exposure of HepG2 cells to the test composition, the cells are analyzed for an increase in glucocorticoid receptors and inhibition of alpha-fetoprotein secretion (Chi et al., Life Sci (1994) 54: 2099-2107). Previously obtained HL data indicated that inhibition of glucocorticoid induced apoptosis correlated deeply with berberine type alkaloids (Miura et al., Biochem Pharmacol (1997) 53: 1315-1322). After an iterative analysis, the data from each analysis will be entered into the algorithm to generate a test HBR array based on the observational data listed, the chemical data, and the data related to the subset of biomarkers.

The quality of the test batch will be judged by comparing test HBR and standard HL HBR array variables for the observation and analysis of the subset, induction of IL-2 receptor and NfkB, HepG2 cells, c-Ki-ras2 gene expression Down-regulation, evidence of increased glucocorticoid receptors and inhibition of alpha-fetoprotein secretion (including data collected for growth conditions, origin and demonstration of berberine alkaloids), terminal differentiation of human embryonic cancer clones into neuronal cells And to predict the equivalent bioreaction effect of the test batch on inhibition of dexamethasone induced apoptosis as shown by standard HL. Based on this process, it can be determined whether the test batch is similar to or different from the quality of the standard.

Example 11 Evaluation of Tang (Show-Psycho-Sat) After Xiao Difference Using Two Bioassays.

To assess the quality of three raw materials of Xiao Chai Hu Tang, two bioassays were used: 1) inhibition of cell growth and 2) hepatitis B virus secretion from infected cells. The Xiao Chai Tang composition is made of a mixture of 6-7 herb plants as follows ( Radix Bupeuri, Rhizoma Pinelliae, Rhizoma Zingiberis, Radix Scutellariae, Fructus Ziziphi, Codonopsis Pilosula, Radix Ginseng and Radix Glycyrrhizae , by weight, phase). See Table 2 for bulk).

Table 2. Composition of Tang after Xiao Cha

Raw material Plant species Radix bupleuri Rhizoma pinelliae Rhizoma zingiberis Radix scutellariae Fructus ziziphi Codonopsis pilosula Radix ginseng Radix glycyrrhizae Relative quantity by weight Singapore One One 0.375 0.375 0.375 - 0.375 0.375 Korea One 0.717 0.571 0.492 - 0.429 - 0.288 Taiwan One 0.25 0.375 0.375 0.25 - 0.375 0.375

Three "recipes" originate from one of Singapore, Korea or Taiwan. Batches were evaluated for their ability and toxicity to inhibit hepatitis B virus as detected by DNA quantification or detection of hepatitis B surface antigen (HbsAg). See Dong et al., Proc Natl Acad Sci USA ( 1991) 88: 8495-8499).

Briefly, 1 g of formulation is added with 10 ml of water. The mixture was boiled for 30 minutes. After centrifugation the supernatant was collected and filtered through a 0.22 μm filter. Two cell types were used: a) 2.2.15 cells secreting hepatitis B virons (provided in person by Prof. G. Ace; see: Ace et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) and b) HepG2 cells (ATCC cat # HB-8065). One to fifty dilutions were used for each analysis. Cell growth inhibition assays were performed for 72 hours. All other procedures were performed as described in Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499. The results of the analysis using three batches are shown in Table 3. Based on these data, Taiwanese acid was chosen as the standard herbal composition because of its low toxicity with the effect of reducing secretion HbsAG (which is proportional to virus release) by more than half.

Table 3. Xiao Chai Tang (Show-Psycho-Sat)

Raw material % Cell growth inhibition Hepatitis B virus inhibition rate (% secreted) HepG2 2.2.15 DNA HbsAG Singapore 73 100 65 38 Korea 13 60 20 42 Taiwan 0 42 0 47

The data shown in Tables 2 and 3 for Taiwan herb compositions constitute initial data for the standardized HBR arrangement of such herbal compositions. Thus, this data set initially included the raw materials of the herbal compositions, the plant species and relative amounts of each herbal composition, and two bioreactions (ie, hepatitis B virus secretion from infected cells).

Using the schematic of FIG. 1 and the procedures described in Examples 1 and 3, additional data were collected for plant-related data, markers, and bioreactions for standard herbal compositions. This additional data is added to the initial standardized HBR array to create an expanded standardized HBR array. Appropriate analysis of the resulting database, as described in the description and examples, may be performed to identify a subset of variables that are most deeply correlated or most closely related to the bioreaction of interest. The batch HBR arrangement may be determined using the method shown in FIG. 2 and the processes of Examples 2 and 4.

The resulting batch HBR sequence can be compared to a standardized HBR sequence to predict the bioreaction of the batch herbal composition.

Example 12 Herb Preparation

The standardized protocol for the herb extraction preparation was as follows: The ingredients of the herbal raw materials with the appropriate proportions were placed in a jacketed reactor and mixed with water at elevated constant elevated temperature while mixing. The solid was separated from the liquid using a 120-mesh screen. The filtrate obtained was collected and then concentrated by evaporation of water under reduced pressure. The concentrate was spray dried at high temperature to give granulated powder. This aggregate was then formulated into the desired dosage form.

Example 13. Evaluation of Huang Cheng Tang

Huang Qing Tang (HQT) is an ancient Chinese herbal preparation consisting of four unique herbs: Scutellariae (scute), Glycyrrhizae (licorice), Paeonie lactiflora pallus (white peony root), and Fructus zizipho (date) . (Table 4). Such herbal preparations have long been used in Asia to treat various gastrointestinal diseases since 300 AD.

Table 4. Herbal Components of TCM Formulation HQT

Scientific name Common name Traditional uses Scutellariae radix Scute Baical Skullcap Root Reduce capillary permeability: Reduce inflammation: Treat enteritis and dysentery: Increase bile secretion to treat jaundice: Relieve muscle spasms for cough Glycyrrhizae Radix (Gancao) Licorice Root Moisturizes the lungs and stops coughing: relieves cramps and stops pain: relieves herb's action; Used for reducing fever and toxic emissions Fructus Ziziphi Date Fruit Diuretic and strengthening effects Paeonie lactiflora pallus radix White Peony Root Pain suppression and sedation; ligament relaxation and blood purification

Biological and Enzyme Analysis

Table 5. Batch Properties (HQT)

Property Batch A Batch B Batch C origin Taiwan, Sun-Ten Taiwan, Sun-Ten Taiwan, Sun-Ten Recipe Standard Standard Boil 30 minutes Plant parts Root Root

Briefly, each batch of 1 g of Huang Wong Tang (HQT) was added with 10 ml of water (1 mg / ml). The mixture was treated as shown in Table 5. After centrifugation the supernatant was collected and filtered through a 0.22 μm filter. The following two cell morphologies were used to test for the biological effects of each batch of HQT: a) Jurkat T cells (ATCC cat # TIB-152) and b) HepG2 cells (ATCC cat # HB-8065). One to fifty dilutions were used for each analysis. Frozen cells (10 ⁷ / ml) were thawed rapidly in a water bath at 37 ° C. The cells were then diluted in 10 ml of pre-warmed medium (Life Technologies, Inc., Catalogue and Reference Guide, 1998-1999, Cell Culture section) and then centrifuged at 1500 rpm for 5 minutes. The supernatant was then discarded and cells were incubated at 37 ° C., 5% CO ₂ in 100 ml medium. After two days, the cells were counted (approximately 8 × 10 ⁵ / ml, 100 ml total).

The batches were also evaluated for their ability to inhibit hepatitis B virus as detected by DNA quantification (Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, 1 g of formulation is added with 10 ml of water. The mixture was boiled for 30 minutes. After centrifugation the supernatant was collected and filtered through a 0.22 μm filter. HepG2.2.15 cells secreting hepatitis B virus (provided by Prof. G. Ace; see, Ace et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) were used in this assay. One to fifty dilutions were used for each analysis. Cell growth inhibition assays were performed for 72 hours. All other procedures were performed as described in Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499.

Β-glucuronidase was analyzed because it is known that HQT has anti-diarrhea properties. 96-well containing 0.1 mM phenophthalein glucuronidate, 70 mMTris-HCl (pH 6.8) and 0.8 ng of dialyzed β-glucuronidase (Source E.Coli , purchased from Sigma ™) Other HQT extracts were added to a final volume of 80 μL in triplicate wells of a 96-well plate. After 2 hours of incubation at 37 ^° C., the reaction was terminated with 200 μl of stop solution containing 0.2 M glycine and 0.2 M NaCl (pH 10.4) and a kinetic microplate at 540 nm. reader) to monitor the OD.

The results of the analysis using three batches are shown in Table 6. Based on these data, HQT sources A and B have a relatively low toxicity with high inhibitory activity compared to batch HQT C (ie, the toxicity to HepG2 cells is approximately five times greater and β than either HQT A or B). Inhibitory activity against glucuronidase is 3.3-fold lower, see Table 6.

Table 6. Biological Analysis of Three Formulations of HQT ^*

E. Coli HepG2 Jurkat HBV ‡ β-glucuronidase DNA HQT A 0.6 1.50 0.76 None HQT B 0.7 1.6 0.81 ND HQT C 2.2 0.32 ND ND * Values indicate IC ₅₀ values ND, not determined ‡, control%

Evaluation of the HQT Effect on Protein Expression

Was added 24 hours prior to drug HepG2 cells (1 x 10 ⁶⁾ to RPMI-1640 culture medium of 3.0 ml in 25 cm ² flasks: inoculate (see Life Technologies, Inc., Catalogue and Reference Guide, 1998-1999, Cell Culture section) (seeding). The cells are treated with a herb medicine or in the absence of a herbal medicine, where the herbal medicine is added at a final concentration of 0.2 mg / ml or 4 mg / ml, respectively. Cells are incubated at 37 ° C. for 24 hours. The medium was removed and the cells washed twice with cold PBS. The cells were harvested into 1 ml PBS and centrifuged at 10,000 rpm for 2 minutes, extracted on ice with buffer containing 50 mM Tris-Cl (pH 7.5), 0.2 mM PMSF and 10% glycerol, and then frozen three times. -Freeze-thaw cycles. Potassium chloride was added to the cell filtrate at a final concentration of 0.15 M before centrifugation. Protein concentration was measured and cell extracts were electrophoresed according to the method of Laemmli ( Nature (1970) 227: 680-685). Western blots were performed by standard techniques known in the art, see for example Sambrook, et al (1989). The antibody used relates to the following proteins: Topo I; Stat (20707); Cyclin B1; MAPK (Ab2) and Nm 23 H1.

4 shows that higher concentrations of HQT A or HQT B act differently on the expression of cyclin B1 polypeptide.

HPLC analysis

Hub batches were analyzed by HPLC using a Beckman ODS Ultrasphere ™ column (5 micron particles, 4.6 mm × 25 cm) and detected with a UV spectrophotometer (Perkin Elmer). Wavelengths for UV detection were monitored at 280 nm and 340 nm. The mobile phase was pumped at 1 ml / min and consisted of Solvent A: H ₂ O and Solvent B: 20% MeOH according to the following gradient: 1) The solvent was 100% Solvent A for the first 5 minutes; 2) the next 10 minutes the solvent composition was changed to 10% solvent A / 90% solvent B; 3) The next 40 minutes the solvent was changed to 10% solvent A / 90% solvent B. Then 100% solvent A is added for 5 minutes. HPLC markers are baicalin and baicalein.

Mass spectrometry

Herbal extracts were analyzed by Mariner ™ ESI-TOF Mass Spectrometry (MS) from PE Biosystems. Control tracings were made using two known active ingredients, Baikallein and Baikal in HQT.

HQT samples in water and acid treated batches were analyzed by HPLC and mass spectrometry. Water treated HQT batches A and B had separate HPLC and MS tracings, while acid treated batches gave a nearly equivalent pattern (data not shown).

algorithm

The data collected form part of the multidimensional analysis used to generate a set of multivariate normal distributions as a means to measure baseline correlations between biological activity and standard HQT chemicals (HPLC and mass spectrometry) and origin / growth characteristics.

Example 14. Each Component

A. Licorice .

Glycyrrhizae radix Evaluation of (Liquorice)

Licorice is useful for moistening the lungs to reduce coughing and for relieving cramps and pain. The properties of the licorice batch used in this example are shown in Table 7.

Table 7. Batch Properties (Liquorice)

characteristic Batch A Batch B Batch C Batch D Botanical name Glycyrrhizae radix Glycyrrhizae radix Glycyrrhizae radix Glycyrrhizae radix origin Inner mongolia Inner mongolia Kin Man Herb Center, USA Kinman Herb Center, USA Manufacturing method Standard Standard Boil 30 minutes Warm H ₂ O, 30 minutes Plant parts Root Root - -

Biological and Enzyme Analysis

To analyze the quality of the herbal raw materials, each herbal extract supernatant was analyzed and the analysis repeated three times. For a given sample to be analyzed, 1 g of herbal powder was dissolved in 10 ml of 80 ° C. deionized water (neutral pH) in a polypropylene tube. The tube was then incubated as shown in Table 7 and then centrifuged to obtain supernatant. The batch of licorice was tested on either or both HepG2 cells (ATCC cat # HB-8065) or Jurkat T cells (ATCC cat # TIB-152). Cells were incubated for 24 hours as described above.

The batch was also evaluated for its ability to inhibit hepatitis B virus, as detected by DNA quantification (Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, 1 g of the formulation was added to 10 ml of water. The mixture was boiled for 30 minutes. After centrifugation, the supernatant was collected and filtered through a 0.22 μm filter. 2.2.15 cells secreting hepatitis B byron (provided by Prof. G. Ace; see, Ace et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) were used in this assay. One to fifty dilutions were used for each assay. Cell growth inhibition assays were performed for 72 hours. All other procedures were performed as described by Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499.

Again, β-glucuronidase was analyzed. Other licorice extracts were 96-well containing 0.1 mM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed beta-glucuronidase (source E.Coli , purchased from Sigma). The triple wells of the plate were added until the final volume was 80 μl and analyzed as above.

The results of the analysis using two batches are shown in Table 8. Based on these data, licorice batch A is more toxic (about 9-fold) to Xerkat cells than batch B and more effective against inhibition of β-glucuronidase.

Table 8. Biological analysis of the four formulations, licorice ^*

E. Coli HepG2 Jurkat HBV ‡ β-glucuronidase DNA Licorice A 1.1 1.07 0.41 None Licorice B ND ND 3.6 ND Licorice C 2.1 ND ND ND Licorice D ND ND> 2.0 53.8 * Value indicates IC ₅₀ value ND, not detected ‡ Control%

Expression analysis

To analyze gene expression, Jurkat T cells were treated with herbal extracts as follows: Jurkat cells (10 ⁷ / ml) were quickly thawed in a 37 ° C. water bath. The cells were then diluted in 10 ml of preheated medium (Life Technologies, Inc., Catalogue and Reference Guide, 1998-1999, Cell Culture section) and centrifuged at 1500 rpm for 5 minutes. The supernatant was then discarded and cells were cultured in 100 ml medium at 37 ° C., 5% CO ₂ . After two days, the cells were counted (approximately 8 × 10 ⁵ / ml, 100 ml total).

Herb extract solutions were prepared as described above (eg 2 g herbal powder (0.1 g / ml) to obtain 20 ml sterile solution). The cells were divided into three flasks at a density of 2.5 × 10 ⁵ / ml, 100 ml / each flask. It was analyzed as a control (no extract) and 10 ml of extract at 10 mg / ml and 1 mg / ml. Again, toxicological results are used to determine "high" and "low" concentrations for a given extract. After the addition of the extract, the cell cultures were incubated for 24 hours under the conditions as described above. The cells were counted and subsequently collected in 50 ml centrifuge tubes. The resulting cell pellet was treated with RNA isolation means to extract mRNA (see, eg, Sambrook et al ., 1989 at pages 7.3-7.39).

Micro array

Microarray printing was performed as follows:

Human gene clones were obtained through distributors from IMAGE Consortium libraries, which contain genes of various tissues. Most of the clones were partially ordered and the sequences were available as sequence tags expressed in GenBank's dbEST database. Clones were cultured and amplified using commercially available primers prior to application to nylon membranes (Chen et al ., Genomics (1998) 51: 313-324). Approximately 10 ng of each amplified target is applied to a positively charged nylon membrane using a personal computer (PC) with controlled alignment system. The array system allows for high density spotting and deposits 31,000 spots on 18 × 27 mm nylon membranes using a 24-pin array instrument.

cDNA probes and membrane hybridization

2 μg of each mRNA sample (mRNA was isolated as described above) was labeled with biotin and / or digoxigenin using random primed reverse transcription. The labeled samples were treated with alkali and the resulting labeled nucleic acid was precipitated prior to use in hybridization. Chen et al . Membrane hybridization and cleaning were performed using labeled probes as disclosed in (1998). Β-galactosidase-conjugated streptavidin (Strept-Gal) and alkaline phosphate to detect spots on the membrane in dual color mode (ie biotin and digoxigenin) Patase-conjugated digoxigenin antibody (anti-Dig-AP) was used. After color development, image digitization using image means was employed (e.g., flatbed scanner or digital camera). Quantitative dimensions were measured by using a program to measure the integration density of the primary color components of each spot, regression analysis of the integration density data, and by computer analysis indicating the location of statistical outliers as differently expressed genes.

Gene Expression Data for Samples 1, 2 and Licorice (ST117)

Extracts 1, 2 and 6 corresponding to the extracts of Cordyceps sinensis, Poria cocos (ST 027) and licorice, respectively, were analyzed by the following method: Batch was assessed for toxicity using Jurkat T cells.

The extract was prepared as described in Example 6. The cells were divided into 24-well culture plates by adding 1 ml of Jurkat cells at a density of 5 × 10 ⁵ / ml. As described the analysis was performed with a control (no extract) and extracts of five concentrations (see Table 9). High and low concentrations for cell culture analysis varied between 10 mg / ml and 0.05 mg / ml (ie mg dry herbs extract per ml) depending on the toxicity of the extract to the cells. For some samples, the "high" and "low" concentrations were down-regulated due to toxicity at 10 mg / ml, but nevertheless, at least tenfold was maintained between the extremes. For example, in the case of licorice (ST117), "high" was 0.5 mg / ml and "low" was 0.05 mg / ml (see Table 9). After the addition of the extract, the cell cultures were incubated for 24 hours under the conditions as described in Example 6. Counting the cells and tabulating the resulting data showed extract toxicity. The resulting data is shown in Table 9.

Table 9. Cell Number Survived at Different Concentrations of Herb Extract Solution

Experimental concentration no. x 10 ⁵ ml 10mg / ml 5mg / ml 2mg / ml 1mg / ml 0.5mg / ml No Drug High Concentration Low Concentration (mg / ml) (mg / ml) 1 Cordyceps sinensismycelium2 ST0273 ST0-444 ST0515 ST0936 ST1177 ST1238 ST1289 ST13410 ST237 8.4 11.9 11.5 9.2 9.0 12.44.2 8.7 10 7.5 10 10- 5.9 8.4 9.9 9.4---1.7 5.4--1.9 3.8 4.4- 1.6 3.6 4.6 5.83.4 6.4 8 9.3 7.83.5 7.7 7.9 7.7 8.32.9 6.1 11.2 9.6 9.8--2.5 6.6 8.7 10 110 15 0.50.5 0.050.5 0.050.5 0.055 0.55 0.55 0.51 0.1

Note: The initial cell number is 5 x 10 ⁵ / ml and the number is 10 x 10 ⁵ / ml after 24 hours of incubation. "-" All represent dead cells.

protocol:

1. Add 1 ml of 5 × 10 ⁵ / ml Jurkat cells to a 24-well culture plate.

2. Prepare and sterilize 12 kinds of herbal extract solution.

3. Test 5 concentrations per sample. 10 mg / ml, 5 mg / ml, 2 mg / ml, 1 mg / ml, 0.5 mg / ml

4. Incubate in a 5% CO ₂ incubator at 37 ° C. for 24 hours.

In each analysis, 144 × 96 genes (ie 13,824 genes) were analyzed (data not shown) and about 100 genes showed a significant difference compared to that of the control (Table 10). Some genes were up regulated and others down. The magnitude of the difference from the control sometimes varied with the relative amount of the herbal composition to which specific cells were exposed. The numbers under C1 (control treatment) and H or L (hub) indicate the amount of mRNA expressed after excluding the background (Table 10). Gene designation can be encoded in the array AD and traced to identify specific gene bank clones. The level of expression was measured by H or L divided by C. Only a fraction of the 13,824 genes in the samples treated with each herb showed significant change, ie increase, decrease or no change (see Table 10).

Table 10.

In this way, we can relate specific gene expression to exposure of cells to none, low (L), or high (H) herbal compositions. Many genes have identified codes for important proteins in known metabolic or biochemical pathways in this way. Many of these proteins have direct and indirect effects on physiological, morphological, and psychological parameters. Thus, this method allows to associate specific genetic fingerprints of herbal compositions with array biological effects. Such association may be used to shape or characterize the herbal composition for purposes of quality control and quality assurance and for evaluation of pharmacological or toxicological properties.

HPLC analysis

Hub batches were analyzed by HPLC using a Beckman ODS Ultrasphere ™ column (5 micron particles, 4.6 mm × 25 cm) and detected with a UV spectrophotometer (Perkin Elmer). Wavelengths for UV detection were monitored at 280 nm and 340 nm. The mobile phase was pumped at 1 ml / min and consisted of Solvent A: H ₂ O and Solvent B: 20% MeOH according to the following gradient: 1) The solvent was 100% Solvent A for the first 5 minutes; 2) the next 10 minutes the solvent composition was changed to 10% solvent A / 90% solvent B; 3) The next 40 minutes the solvent was changed to 10% solvent A / 90% solvent B. Then 100% solvent A was added for 5 minutes. HPLC markers are glycyrrazine.

algorithm

The data collected is part of the multidimensional analysis used to generate a set of multivariate normal distributions as a means to measure baseline correlations between biological activity and standard licorice molecules, chemicals (HPLC and mass spectrometry), and origin / growth characteristics. Form.

B. Scute

Radix Scutellarie Radix Scutellariae Evaluation of (skew)

Squats have been found to be useful in relieving capillary permeability and inflammation. It can also be used to treat enteritis and dysentery and increase bile secretion to treat jaundice; Relieves muscle spasms; It can be used to treat coughs and release parasites. The characteristics of the skew arrangement used in this example are shown in Table 11.

Table 11. Deployment Properties (Skew)

characteristic Batch A Batch B Batch C Batch D Botanical name Scutellariae radix Scutellariae radix Scutellariae radix Scutellariae radix origin Sanxi Province Kinman Herb Center, USA Kinman Herb Center, USA USA, Boston Manufacturing method Standard Boil for 30 minutes Warm H ₂ O, 30 minutes Boil for 2 hours Plant parts Root - - -

Biological and Enzyme Analysis

Briefly, each formulation of 1 g of skew extract was added with 10 ml of water (1 mg / ml). The mixture was treated as shown in Table 11. After centrifugation the supernatant was collected and filtered through a 0.22 μm filter. Placement of the squats were tested for either or both HepG2 cells (ATCC cat # HB-8065) or Jerkat T cells (ATCC cat # TIB-152). One to fifty dilutions were used for each assay. Cells were incubated for 24 hours as described above.

The batch was also evaluated for its ability to inhibit hepatitis B virus as detected by DNA quantification (Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, 1 g of formulation was added with 10 ml of water. The mixture was treated as shown in Table 11. After centrifugation, the supernatant was collected and filtered through a 0.22 μm filter. 2.2.15 cells secreting hepatitis B byron (provided by Prof. G. Ace; see, Ace et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) were used in this assay. One to fifty dilutions were used for each assay. Cell growth inhibition assays were performed for 72 hours. All other procedures were performed as described by Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499.

For β-glucuronidase, another skew extract was prepared with 0.1 mM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed β-glucuronidase (Source E.Coli). To a triple well of a 96 well plate containing, purchased from Sigma) to a final volume of 80 μl. After 2 hours of incubation at 37 ° C., the reaction was terminated with 200 μl of stop solution containing 0.2 M glycine and 0.2 M NaCl (pH 10.4), and OD was monitored with a dynamic microplate reader at 540 nm.

The results of the analysis using three batches are shown in Table 12.

Table 12. Biological Assays for Four Skew Formulations ^*

E. Coli HepG2 Jurkat HBV ‡ β-glucuronidase DNA Skew A 1.5 0.33 0.45 None Skew B 1.8 ND ND ND Skew C 0.3 ND ND ND Skew D ND 0.65 ND 27.5 * Value indicates IC ₅₀ value ND, not detected ‡ Control%

Evaluation of the Skew Effect on Protein Expression

24 hours before the addition of the extract, HepG2 cells (1 × 10 ⁶ ) were seeded in 3.0 ml of RPMI-1640 medium in a 25 cm ² flask (Life Technologies, Inc., Catalog and Reference Guide, 1998-1999, Cell Culture section)

Cells were treated with or without the herbal agent, and when treated with the herbal agent, the agents were added at two final concentrations of 0.2 mg / ml or 4 mg / ml, respectively, and incubated at 37 ° C. for 24 hours. The medium was removed and the cells washed twice with cold PBS. The cells were harvested into 1 ml PBS and centrifuged at 10,000 rpm for 2 minutes, extracted on ice with buffer containing 50 mM Tris-Cl (pH 7.5), 0.2 mM PMSF and 10% glycerol, and then frozen three times. -Defrost cycle. Potassium chloride was added to the cell filtrate at a final concentration of 0.15 M before centrifugation. Protein concentration was measured and cell extracts were electrophoresed according to the method of Laemry UK ( Nature (1970) 227: 680-685). Western blots were performed by standard techniques known in the art, see for example Sambrook, et al (1989). The antibodies used relate to the following proteins: Topo I; Stat (20707); Cyclin B1; MAPK (Ab2) and Nm 23 H1.

4 shows that Skew batches A and B have no particular effect on the expression of the polypeptides analyzed for Western blot.

HPLC analysis

Hub batches were analyzed by HPLC using a Beckman ODS Ultrasphere ™ column (5 micron particles, 4.6 mm × 25 cm) and detected with a UV spectrophotometer (Perkin Elmer). Wavelengths for UV detection were monitored at 280 nm and 340 nm. The mobile phase was pumped at 1 ml / min and consisted of Solvent A: H ₂ O and Solvent B: 20% MeOH according to the following gradient: 1) The solvent was 100% Solvent A for the first 5 minutes; 2) the next 10 minutes the solvent composition was changed to 10% solvent A / 90% solvent B; 3) The next 40 minutes the solvent was changed to 10% solvent A / 90% solvent B. Then 100% solvent A is added for 5 minutes. HPLC markers are Baikal and Baikallein.

The skew batches in the water treated and acid treated samples were analyzed by HPLC. Water treated batches and acid treated batches are virtually indistinguishable.

algorithm

The data collected form part of the multidimensional analysis used to generate a set of multivariate normal distributions as a means to determine baseline correlations between biological activity and standard skew chemicals (HPLC), and origin / growth characteristics.

C. Earl Root

Paeonie lactiflora pallus radix Evaluation of (peony)

Use peony to suppress and calm pain. Peony is also known to relax the ligaments and cleanse the blood. The properties of the peony used in this example are shown in Table 13.

Table 13. Deployment Characteristics (Peason)

characteristic Batch A Batch B Botanical name Paeonie lactiflora pallus Paeonie lactiflora pallus origin Anwey Province Boston, USA Manufacturing method Standard Boil 2 hours Plant parts Root Root

Biological and Enzyme Analysis

Briefly, each formulation of 1 g peony extract was added with 10 ml of water (1 mg / ml). The mixture was treated as shown in Table 13. After centrifugation the supernatant was collected and filtered through a 0.22 μm filter. The batch of peony was tested for either or both HepG2 cells (ATCC cat # HB-8065) or Jerkat T cells (ATCC cat # TIB-152). One to fifty dilutions were used for each assay. Cells were incubated for 24 hours as described above.

The batch was also evaluated for its ability to inhibit hepatitis B virus as detected by DNA quantification (Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499).

Briefly, 1 g of formulation was added with 10 ml of water. The mixture was treated as shown in Table 13. After centrifugation, the supernatant was collected and filtered through a 0.22 μm filter. 2.2.15 cells secreting hepatitis B byron (provided by Prof. G. Ace; see, Ace et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) were used in this assay. One to fifty dilutions were used for each assay. Cell growth inhibition assays were performed for 72 hours. All other procedures were performed as described by Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499.

Another peony extract was prepared in a 96-well plate containing 0.1 mM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed β-glucuronidase (source E.Coli , purchased from Sigma). Was added to a final volume of 80 μl. After 2 hours of incubation at 37 ° C., the reaction was terminated with 200 μl of stop solution containing 0.2 M glycine and 0.2 M NaCl (pH 10.4), and OD was monitored with a dynamic microplate reader at 540 nm.

The results are shown in Table 14.

Table 14. Biological Analysis for Two Peony Formulations ^*

E. Coli HepG2 Jurkat HBV ‡ β-glucuronidase DNA Peony A 2.8> 1.5 1.1 None Peony B> 2.5 ND ND ND * Value indicates IC ₅₀ value ND, not detected ‡ Control%

HPLC analysis

Beckman ODS Ultrasphere Hub batches were analyzed by HPLC using columns (5 micron particles, 4.6 mm × 25 cm) and detected by UV spectrophotometer (Perkin Elmer). Wavelengths for UV detection were monitored at 280 nm and 340 nm. Mobile phase was pumped at 1 ml / min and solvent A: H according to the following gradient₂O and solvent B: consisted of 20% MeOH: 1) The solvent was 100% solvent A for the first 5 minutes; 2) the next 10 minutes the solvent composition was changed to 10% solvent A / 90% solvent B; 3) The next 40 minutes the solvent was changed to 10% solvent A / 90% solvent B. Then 100% solvent A was added for 5 minutes. HPLC markers are paeoniflorin.

Peony batches were analyzed by HPLC as shown in FIG. 5.

algorithm

The data collected form part of the multidimensional analysis used to generate a set of multivariate normal distributions as a means to measure baseline correlations between biological activity and standard peony chemicals (HPLC), and origin / growth characteristics.

D. Date Fruit

Jiji Pie fructus Ziziphi Fructus Evaluation of date palm fruit

Date palm fruits have been used for diuretic properties and fortifying effects. The properties of the date palm arrangement used in this example are shown in Table 15.

Table 15. Batch Properties (Diamond Fruit)

characteristic Batch A Batch B Batch C Botanical name Ziziphi Fructus Ziziphi Fructus Ziziphi Fructus origin Hebei Province Kinman Herb Center, USA Kinman Herb Center, USA Manufacturing method Standard Boil for 30 minutes Warm H ₂ O, 30 minutes Plant parts Fruit - -

Biological and Enzyme Analysis

Briefly, each batch of 1 g date palm fruit extract was added with 10 ml of water (1 mg / ml). The mixture was treated as described in Table 15. After centrifugation the supernatant was collected and filtered through a 0.22 μm filter. Placement of date palm fruit was tested on either or both HepG2 cells (ATCC cat # HB-8065) or Jerkat T cells (ATCC cat # TIB-152). One to fifty dilutions were used for each assay. Cells were incubated as described above for 24 hours.

The batch was also evaluated for its ability to inhibit hepatitis B virus as detected by DNA quantification (Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, 1 g of formulation is added with 10 ml of water. The mixture was treated as described in Table 15. After centrifugation, the supernatant was collected and filtered through a 0.22 μm filter. 2.2.15 cells secreting hepatitis B byron (provided by Prof. G. Ace; see, Ace et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) were used in this assay. One to fifty dilutions were used for each assay. Cell growth inhibition assays were performed for 72 hours. All other procedures were performed as described by Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499.

Other date palm fruit extracts were prepared with 0.1 mM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed β-glucuronidase (source E.Coli , purchased from Sigma). To a triple well of a well plate was added to a final volume of 80 μl. After 2 hours of incubation at 37 ° C., the reaction was terminated with 200 μl of stop solution containing 0.2 M glycine and 0.2 M NaCl (pH 10.4), and OD was monitored with a dynamic microplate reader at 540 nm. The results are shown in Table 16.

Table 16. Biological Analysis for Three Date Palm Fruit Formulations ^*

E. Coli HepG2 Jurkat HBV ‡ DNAβ-glucuronidase Date Fruit A 1.2 1.5 5.1 None Date Fruit B ND> 2.0 ND 52.3 Date Fruit C 2.5 ND ND ND * Value indicates IC ₅₀ value ND, not detected ‡ Control%

HPLC analysis

Hub batches were analyzed by HPLC using a Beckman ODS Ultrasphere column (5 micron particles, 4.6 mm × 25 cm) and detected by UV spectrophotometer (Perkin Elmer). Wavelengths for UV detection were monitored at 280 nm and 340 nm. The mobile phase was pumped at 1 ml / min and consisted of Solvent A: H ₂ O and Solvent B: 20% MeOH according to the following gradient: 1) The solvent was 100% Solvent A for the first 5 minutes; 2) the next 10 minutes the solvent composition was changed to 10% solvent A / 90% solvent B; 3) The next 40 minutes the solvent was changed to 10% solvent A / 90% solvent B. Then 100% solvent A was added for 5 minutes. HPLC markers for date palm fruit are chelidonic acid and cAMP.

Date fruit batch samples were analyzed by HPLC as shown in FIG. 6.

algorithm

The data collected form part of the multidimensional analysis used to generate a set of multivariate normal distributions as a means to measure baseline correlations between biological activity and standard date palm fruit chemicals (HPLC), and origin / growth characteristics.

Example 15 Characterization of Herbal Pharmaceuticals by Nucleic Acid Microarray Analysis.

Introduction .

Rapid development of nucleic acid microarray technology has led to a surge in gene expression data (Lander, 1999, Duggan et al., 1999). Four characteristics of the gene expression illustrate the significant value of using nucleic acid microarrays to study the gene expression profile. (Iii) Nucleic acid microarrays make it easier to measure thousands of gene transcripts at a time. (Ii) The close relationship between gene production function and gene expression pattern makes it possible to predict gene function. (Iii) Cells respond to microenvironmental changes by changing the level of expression of specific genes. (Iii) Intracellularly expressed gene sets measure what the cell is from and what biochemical and regulatory systems are involved (Brown and Bostein, 1999). By using a microarray system, these features can be studied in their entirety.

Expression of the desired gene number can be detected using nucleic acid microarray techniques. For example, up to about 20,000 genes may be located on a single array. We have developed a nucleic acid microarray (Microarray / CD) with a colorimetric detection system (Chen et al., 1998). Gene expression profiles of other cell lines were studied using a microarray filtration membrane (2.7 cm x 1.8 cm) with about 10,000 cDNAs representing about 10,000 distinct human transcripts. The sensitivity and detection limits of the microarray / CD system are characteristic and comparable to those with radioactive detection or systems with laser induced fluorescence detection (Bertucci et al., 1999).

As mentioned above, the gene expression profile of a cell is indicative of the cell's origin, current differentiation, and response of the cell to external stimuli. In other words, the gene expression is indicative of the state of the cell and microarrays are the perfect means for expression purposes. In this study, we used the microarray / CD system to characterize the response of cells when the external stimulus is a Chinese herbal medicine. In contrast, we also classified other herbal medications based on the stimulated gene expression profile.

FIG. 7 is a flowchart depicting a general method to be used to establish expression response data sets for cells treated with the herbal composition. The method;

(a) measuring the IC ₅₀ concentration of the herbal composition by incubating the various concentrations of the herbal medicine in a mammalian cell culture and identifying the concentration remaining 50% of the viable cells after a predetermined time,

(b) incubating the mammalian cells with herbal extracts of various IC ₅₀ concentration ratios,

(c) harvesting and counting the cultured cells after a predetermined incubation time,

(d) lysing immediately after removing the cells from the incubator and extracting mRNA from the cell filtrate,

(e) labeling and translating the mRNA into labeled cDNA by reverse transcription mechanism,

(f) mixing the labeled cDNA with a regulatory cDNA of plant raw material and performing hybridization on microarrays of mammalian gene probes,

(g) determining the expression level of the gene by analyzing the digitized image of the microarray hybridization result,

(h) performing data preprocessing to select data for statistical analysis,

(i) obtaining expression data generated by microarray experiments of the herbal compositions at various concentrations,

(j) data pretreatment to select the gene with statistical significance in cells treated with different concentrations of the herbal agent,

(k) classifying expression profiles into clusters by statistical methods, such as self-organizing map algorithms,

(l) inferring a characteristic expression profile for the herbal agent based on the expression profile cluster.

8 is a flowchart illustrating how data sets of expression data for various batch herbal compositions are integrated to create an expression profile database for a particular herbal composition. The expression profile database then becomes part of the HBR arrangement.

HBR arrays containing expression profiles can also be used to identify unknown herbal compositions. 9 is a flowchart illustrating a general method for identifying unknown herbal compositions, the method comprising:

(a) constructing an HBR sequence comprising the collection of a characteristic profile for a herbal drug or a profile of various herbal drugs by the steps described above,

(b) obtaining a characteristic expression profile data set of the unknown pharmaceutical composition,

(c) comparing the HBR sequence containing the characteristic expression profile derived by the unknown herbal composition with a standardized HBR sequence containing expression data by an algorithm such as a Hamming distance algorithm,

(d) recording the possible arrangements to identify characteristic expression profiles of the most likely herbal compositions recorded in the HBR arrangements.

Recording probable arrangements of HBR sequences containing expression profiles can be performed using hierarchical cluster analysis of the Hamming distance matrix. The use of hierarchical cluster analysis for Hamming distance matrices is known in the art.

The gene expression profile can also be integrated into the normalized HBR arrangement. As already discussed, standardized HBR arrangements containing such genetic profiles induced by herbal compositions allow for the study of pharmacological mechanisms of herbal compositions, the discovery of new applications of herbal compositions, and the design of optimized formulations of complex herbal formulations. Can be used for As can be seen from the flowchart of FIG. 10, the method includes;

(a) constructing a data set containing the characteristic gene expression profile for the herbal composition,

(b) recording each gene by concordance of a gene expression profile in said data set using known statistical parameters such as variation coefficients,

(c) Gene expression profiles for herbal medicines based on statistical records can be summarized in their entirety by including the step of selecting to integrate into the standardized HBR sequence.

As summarized in the flowchart of FIG. 11, HBR arrays containing gene expression profiles can also be used to identify signature gene expression profiles induced by respective chemical components in a herb composition consisting of complex chemical components. The method;

(a) constructing an HBR sequence containing the characteristic gene expression profile for the herbal composition by the steps described above,

(b) determining the composition of the chemical component in the herb drug by high performance liquid chromatography (HPLC) or liquid chromatography mass spectrometry (LC-MASS),

(c) repeating step (b) for various batch hub pharmaceutical formulations

(d) recording the correlation coefficient between the expression level of each gene in the amount of each chemical component in the herbal formulation,

(e) selecting a Pearson correlataion coefficient whose expression profile for each chemical component is greater than 0.99 or less than -0.99.

Any herbal composition can then be characterized through the use of gene expression profiles generated through the use of nucleic acid microarrays. Moreover, any number of genes that are expressed differently can be selected to be included in the data set representing the gene expression profile. For example, any gene number can be selected from about 10, about 100, about 500, about 1000, about 1500, about 2000, about 2500, or more or between.

Prescriptions from the Chinese herbal herbal medicine, Squat and Licorice (Huang Chin Tang), stop diarrhea, relieve seizures and reduce fever. Huang Chintang's ingredients are skew, peony, licorice and date palm fruit. This prescription has been in use for more than 1000 years, but chemical biomedical clinical research on the prescription has only been carried out more than a decade ago. In this study, we studied gene expression profiles of herbal pharmaceutical treated cells using nucleic acid microarray techniques. The goal of the study is to verify the availability of the microarray / CD system for classification of other herbal compositions or other preparations and to find predictor genes (marker genes) for Huangchintang prescriptions. The long-term goal is to discover the interrelationship of biochemical components in each herbal composition with gene expression profiles of variously treated cells and to decipher the molecular pharmacological mechanisms of Chinese herbal medicines in a rational way.

Materials and methods.

1. Development of Cell Banking System

Purpose : Microarray system is a highly sensitive method for examining gene expression patterns in cells. In order to minimize cell variability for herbal drug testing, it is necessary to establish cell banking systems with host cell banks (MCBs) and working cell banks (WCBs).

Scope: The cell bank system is used for all cell types in microarray studies.

Device: CO ₂ air-jacket incubator (NUAIRE ™ DH auto inlet)

Centrifuge (KUBOTA 2100)

Cooling vials (Corning Costar, cat. # 430659)

750 ml of tissue culture flask (Falcon, cat. # 3045)

Tissue culture dish 150 × 25mm (Falcon, cat. # 3025)

Cells: Xerkat T cells from Dr. Alexander Ho

Reagent: RPMI medium 1640 (GIBCO BRL, cat. # 31800-014),

Dimethyl sulfoxide (DMSO) (Sigma, cat. # D-2650),

Fetal bovine serum (HyClone, cat. # SH30070.03, Lot # AGL7258),

2-mercaptoethanol (Zibco BRL, cat. # 21985-023, 5 x 10 ^-2 M)

Medium: I. Culture Medium: 90% RPMI + 10% Fetal Bovine Serum + 2-Mercaptoethanol (5 × 10 ^-2 M)

II. Cooling medium: 90% RPMI 1640 + 10% DMSO

step:

A. Host Cell Bank

1. Follow standard sterilization procedures for cell culture.

2. Inoculate Xerkat T cells into medium in flasks in a 37 ° C. 5% CO ₂ incubator.

3. After incubation for two days, count the cells and dispense the cells into two flasks. caution. Cell density of about 5 × 10 ⁴ per ㎖ - Maintain 2 × 10 ⁶ pieces.

4. Incubate until the cell number reaches 2 x 10 ⁷ and count the cell number.

5. Collect the cells in a 50 ml centrifuge tube and run at 1300 rpm (300 x g) for 5 minutes.

6. Discard the supernatant and resuspend the cell pellet in cold cooling medium. The number of cells in each vial is about 1 × 10 ⁶ cells per ml.

7. Temperature profile below: Slowly cool the cells by 2 hours at -20 ° C and 24 hours at -80 ° C and then place the cells in a liquid nitrogen (N ₂ ) reservoir. A total of 20 frozen vials are stored in the MCB.

B. Working Cell Bank

1. Take one vial of cells from MCB in a liquid nitrogen (N ₂ ) tank and dissolve in a water bath at 37 ℃.

2. Transfer the cells to 10 ml warm medium.

3. Turn the cells to 1300 rpm (300 x g) for 5 minutes. The supernatant is discarded. The cells are incubated in 20 ml medium in a flask.

4. Sub-culture the cells into two flasks.

5. Inoculate 5 × 10 ⁷ cells into 500 ml medium in each flask while stirring the entire two flasks. The cells are incubated for two days.

6. Incubate until the cell density is 1 × 10 ⁶ / ml and the total volume reaches 1L.

7. Prepare cooling medium by adding 100 ml of fetal bovine serum and 10 ml of DMSO.

8. Discard the supernatant by centrifugation and resuspend the cells in 110 ml cooling medium.

9. Dispense 1 ml into each cooling vial (10 million cells per vial) for a total of 100 vials. The cells are slowly cooled by the temperature profile described above.

2. Measurement of Growth Inhibitory Concentration of Herb Extracts in Cell Medium

Purpose : Most drugs are toxic to cells. This experiment was designed to test the toxicity of Xerkat T intracellular herbal extracts and to measure the growth inhibitory concentrations of herbal extracts that allow cells to survive.

Scope: This assay can be used to test for toxicity in all kinds of herbal extracts.

Device: CO ₂ air-jacket incubator (NUAIRE ™ DH auto inlet)

Counting chamber (Hemacytometer, Reichert, USA)

Microscope (Zeiss, Axiovert 100)

Cell: Jurkat T Cell

Reagent: RPMI Medium 1640 (Zibco BRL, cat. # 31800-014)

Fetal bovine serum (HyClone, cat. # SH30070.03, Lot # AGL7258)

2-mercaptoethanol (Zibco BRL, cat. # 21985-023, 5 × 10 ^-2 M)

Medium: 90% RPMI + 10% fetal bovine serum + 2-mercaptoethanol (5 × 10 ^-2 M)

Disposable Sterile Syringe Filtration (0.2m, Corning, cat. # 21052-25)

Herb Extract:

1. Cordyceps Sinensis Mycelium

2. ST 024:

3.ST 044:

4.ST 051:

5. ST 093:

6. ST 117:

7. ST 123:

8. ST 128:

9. ST 134:

10.ST 237:

11.PHY906-303503: complex mixture consisting of 4,6,7,10

12.PHY906-284003: Complex mixture consisting of 4,6,7,10

step:

A. Herb Extract Preparation

1. Dissolve 1 g of herb powder in 10 ml of deionized water (neutral pH) at 80 ° C in a polypropylene tube.

2. Incubate the tube slowly for 30 minutes in an 80 ° C. bath and centrifuge at 4000 rpm (1500 × g) for 5 minutes to obtain supernatant.

3. Centrifuge at 11000 rpm (14000 × g) for 10 minutes to collect the supernatant.

4. Filter the supernatant using a disposable sterile syringe.

B. Cell Viability Test

1. Incubate Jerkat T cells as described above.

2. Dispense 1 ml of 5 × 10 ⁵ / ml cells per well into a 24-well culture plate.

3. Prepare 12 kinds of herbal extract solutions. The extract solution must be freshly prepared and used immediately.

4. Add 100, 50, 20, 10, 5 μl of each herb extract solution to the 24 well culture plate to obtain 5 different concentrations of 10, 5, 2, 1, 0.5 mg / ml.

5. Incubate the cells for 24 hours at 37 ° C. in an incubator filled with 5% CO ₂ .

6. Count the cells per well. 10 μl of cell solution is mixed with 10 μl of Trypan blue dye and placed in a cell counting chamber.

7. Count four large square areas to calculate the cell number.

(Number of cells in 4 areas) / 4 × 10 ⁴ × dilution factor = number of cells per ml

3. Profiling Gene Expression Patterns of Jurkat T Cells Treated with Herbal Extracts

Purpose : To profile the gene expression pattern of Jerkat T cells treated with herbal extracts. High density nucleic acid microarrays with colorimetric detection systems are used.

Device: Heat Block (Boekel, Model 110002)

Spectrophotometer (Beckman, DV640)

Centrifuge (KUBOTA 1910)

Bathtub (SLM AMINCO, Model 800)

Hybridization incubator (YIH DER OH-800)

Heat Sealer (TISH-300, TEW)

Reagent: RNAzol ™ B (Tel-Test, cat. # CS-104)

Oligotex mRNA Midi Kit (Qiagen, Hilden, Germany)

Hybridization Bags (Gibco BRL, cat. # 18278-010)

EasiSeal (Hybaid, cat. # HBOSSES1E)

Glass slides (Matusunami, S2214, Japan)

Aerosol ART tip (Molecular bio-products, cat., # 2139)

Random hexamer primer (Zibco BRL, cat. # 48190-011)

Reverse transcriptase and 5 × buffer (Zibco BRL, cat. # 18064-014)

RNase inhibitor (Zibco BRL, cat. # 10777-019)

Biotin-16-dUTP (Boehringer Mannheim, cat. # 1093070)

Dig-11-dUTP (Beringer Mannheim, cat. # 1558706)

Blocking powder for hybridization (Berringer Mannheim, cat. # 1096176)

Bovine Serum Albumin (Sigma, cat. # A2153)

20X SSC (Amresco, cat. # 0918S-2-20XPTM5L)

SDS (Merck, cat. # 113760)

Dextran Sulfide (Sigma, cat. # D6001)

Streptavidin-beta-galactosidase (Zibco BRL, cat. # 19536-010)

Anti-digoxigenin (Beringer Mannheim, cat. # 1093274)

X-gal (Zibco BRL, cat. # 15520-018)

Maleic acid (Sigma, cat. # M1125)

N-lauroylsarcosin (Sigma, cat. # L5777)

Fast red TR / AS-MX board kit (PIERCE, cat. # 34034)

Polyethylene Glycol (Sigma, cat. # P2139)

Reagent Preparation:

1 × Hybridization Buffer (4 × SSC, 0.1% N-lauroylsarcosine, 0.02% SDS, 1% BM Blocking Reagent)

20 x SSC16 ml

1% N-lauroyl sarcosine 8ml

10% SDS160μl

BM Blocking Powder0.8g

H ₂ O51 ml

80 ml total

Heat to 65 ° C. to dissolve the powder and then store at −20 ° C.

50% PEG-8000 (polyethylene glycol)

PEG-80010g

H ₂ O20 ml

After dissolving by heating to 65 ℃ autoclave. Divided and stored at -20 ° C.

10 x TBS (100 mM Tris, 1.5 M NaCl, pH 7.4)

Tris base12.1 g

NaCl87.6g

H ₂ O1000ml

120 mM X-gal

X-gal 100 mg

2 ml of DMF

Store at -20 ° C.

X-Gal Substrate Buffer(1 mM MgCl in 1X TBS buffer)₂, 3mM K₃Fe (CN)₆, 3mM K₄Fe (CN)₆) 500 ml

10X TBS / pH 7.450 ml

Potassium Ferrocyanide (633.5 mg)

Potassium Ferricyanide (493.9 mg)

MnCl ₂ 101.6 mg

After filtration and storage at -20 ℃.

BM blocking dilution buffer / pH7.5 (0.1M maleic acid, 0.15M NaCl)

100 ml of 1M maleic acid

5M NaCl30ml

7.5 g of solid NaOH

H _{2 up to} 01000ml

10% blocking reagent 100 ml

Blocking Powder 10g

100 ml of blocking dilution buffer (but not between 20)

After heating to 70 ℃ autoclave sterilization. Store at 4 ° C.

20% Dextran Sulfate

2 g of dextran sulfate

To H ₂ 08ml

After autoclaving, store at -20 ℃.

DEPC-treated water 800 ml

400 μl diethyl pyrocarbonate (stored at 4 ° C)

H ₂ 0800ml

Place it in a shaking 37 ° C bath for 4 hours and place in a 37 ° C greenhouse overnight. The solution is autoclaved for 2 times 45 minutes or 25 minutes each.

step:

A. Preparation of Jerkat T Cells

1. Dissolve 1 vial of Jerkat T cell. Transfer to 10 ml growth medium. The number of vials you want to thaw depends on the number of tests you want to run. In general, one vial of cells is needed for two herbal extract tests.

2. Resuspend the cells in 50 ml medium.

3. Incubate the cells for one day. 150 ml of medium is added and aliquoted into two 100 ml flasks each.

4. Incubate the cells for 3 days.

5. Replace the medium and dispense into four flasks of 100 ml medium each.

6. Incubate the cells for 2 days.

7. Count the cells. The cells are collected and spun down. The cell pellet is resuspended in medium to reach 5 × 10 ⁵ cells per ml and up to 100 ml per flask.

8. Incubate the cells for 3 hours before adding the herbal extract.

B. Herb Extract Treatment

Prepare herbal extracts (see Herb Extract Preparation).

2. Calculate the 50% growth inhibitory concentration of each herbal extract according to the growth inhibitory concentration of the herbal extract determined by cell survival experiment.

3. Define the 50% growth inhibition concentration as H. Cells are treated with serial dilutions of herbal extracts at the following concentrations: H, H / 2.5, H / 5, H / 10, H / 20.

4. Incubate the cells for 24 hours.

5. Collect cells and count the number of cells. Centrifuge to obtain cell pellets.

6. Wash the cells with 1 × PBS once.

7. Discard the supernatant. The cell pellet is ready to isolate total RNA.

C. Isolation of Total RNA

1. Add 1 ml of RNAzol ™ per 10 ⁷ cells. The cell pellet is homogenized without vortex.

2. Add 0.1 ml of chloroform per ml of homogenate, cover the sample tightly and shake (not vortex) for 1 minute. Leave on ice for 15 minutes.

3. Centrifuge at 12000 rpm (13500 x g) at 4 ° C for 15 minutes.

4. After centrifugation, the homogenate is divided into two phases: bottom blue phenolchloroform phase and colorless top water soluble phase. DNA and proteins are in the intermediate and organic phases. Transfer the aqueous phase to a new tube, add the same volume of isopropanol, and store the sample at -80 ° C. caution. The range of addition of isopropanol is from 0.7 to 1 volume of the aqueous phase solution.

5. Samples are stored at -80 ° C until use. Dissolve the sample completely before centrifugation and mix 2-3 times with the tube inverted. Centrifuge the samples for 15 minutes at 13000 rpm (15000 x g).

6. Remove the supernatant and wash the RNA pellet once with 1 ml of 75% ethanol. Centrifuge for 3 min at 13000 rpm (15000 x g) at 4 ° C.

7. Discard the supernatant. The pellet is dried under vacuum for 1 minute. caution. Do not dry the RNA pellet completely. The solubility of the RNA pellet is greatly reduced.

8. The RNA pellet is dissolved in 50-100 μl of diethylpyrocarbonate (DEPC) -treated water by pipetting. caution. If the pellets are difficult to dissolve, it is helpful to antigenize the pellets at 60 ° C. for 10-15 minutes.

9. Measure the absorbance at 260 nm (A260) and 280 nm (A280) with a spectrophotometer.

Concentration analysis: OD ₂₆₀ x 40 ng / μL x Dilution factor = total RNA (ng / μl).

D. Isolation of Poly-A + mRNA from Total RNA

1. Determine the amount of eluent RNA to be added to the RNA solution and the appropriate volume of buffer OBB and oligotex suspension solution according to Table 17.

Table 17. Buffer Amounts for Oligotex mRNA Spin-Column Protocols

Full RNA Add water without RNase: (μl) Buffer OBB (μl) Oligotex Suspension (μl) Manufacture size 20 µg 100 100 6 small type 0.25mg 250 250 15 small type 0.25-0.5mg 500 500 30 middle 0.5-0.75mg 500 500 45 middle 0.75-1 mg 500 500 55 middle

The following procedure is based on the use of 500 μg total RNA as an example.

2. Add 500 μL of 2 × binding buffer and 30 μL of oligotex suspension to the total RNA sample. Mix the contents thoroughly by soaking the tube.

3. Incubate the sample at 70 ° C. for 10 minutes.

4. Incubate for 20 minutes at room temperature.

5. Centrifuge for 2 minutes at full speed (14000 to 18000 x g) and aspirate the supernatant.

6. Resuspend the pellet in 400 μl wash buffer OW2 and transfer to a spin column to centrifuge the spin column for 1 minute.

7. Wash with 400 μl OW2 and centrifuge as above.

8. Add 20 μl of preheated (70 ° C.) elution buffer on the column and resuspend in resin. Close the microcentrifuge tube.

9. Place the spin column at 70 ° C. in a 1.5 ml microcentrifuge tube for 3 minutes.

10. Centrifuge the column at full speed for 2 minutes at room temperature.

11. Elute again (repeat steps 8 to 10 for better yield)

E. cDNA Labeling

1.2 μg of mRNA, for monochrome labeling (Hat22: 1 × 10 ⁹ , Rbcl: 5 × 10 ⁹ , Ga 4: 1 × 10 ⁸ , Rca: 5 × 10 ⁷ , Asa1: 1 × 10 ⁷ , Atps: 5 × 10 ⁶ molecules / μl) 1 μl of control plant mRNA, ⁶ μl of 50 mM irregular hexamer, and DEPC-H ₂ 0 are mixed to make a final volume of 28.88 μl. For bi-color mode, separate addition of 2 μg mRNA and control plant mRNA to biotin or Dig labeling respectively: 1. Biotin labeling: Hat22: 1 × 10 ⁸ , Rbcl: 5 × 10 ⁷ , Ga4 : 2 × 10 ⁷ , Rca: 1 × 10 ⁷ , Asa1: 1 × 10 ⁷ , Atps: 1 × 10 ⁷ , Hat4: 1 × 10 ⁷ / μl, 2. Dig labeling: Hat22: 1 × 10 ⁷ , Rbcl: 1 × 10 ⁷ , Ga 4: 1 × 10 ⁷ , Rca: 1 × 10 ⁷ , Asa 1: 1 × 10 ⁸ , Atps: 5 × 10 ⁷ , Hat 4: 2 × 10 ⁷ / μl.

2. Denature at 70 ° C. for 10 minutes and then cool rapidly on ice for 5 minutes.

3. 10 μl of 5 × 1 standard buffer, 5 μl of 0.1 M DTT, 1 μl of 0.25 M dATP, dCTP, dGTP mixture, 1 μl of 2 mM dTTP, 1 mM Biotin-16-dUTP or Dig-11 2 μl of -dUTP (1 mM), 0.63 μl of 40 U / μl RNAsin and 1.5 μl of Superscript II (reverse transcriptase, Zibco BRL) (200 U / μl) are added.

4. Mix well and incubate at 25 ° C. for 10 minutes and then incubate at 42 ° C. for 90 minutes.

5. Stop the reaction for 5 minutes at 94 ° C.

6. Add 5.5 μL of 3M NaOH at 50 ° C. for 30 minutes.

7. Add 5.5 μL of 3M CH ₃ COOH at 50 ° C. for 30 minutes.

8. Settle the labeled cDNA by adding 34 μl of water, 50 μl of 7.5 M ammonia acetate, 10 μg of linear polyacrylamide and 380 μl of alcohol anhydride as carrier.

9. Incubate the sample at -80 ° C for 30 minutes. Centrifuge at 13000 rpm for 15 minutes.

10. Wash the pellet with 1 ml of 70% ethanol and centrifuge at 13000 rpm for 5 minutes.

11. Dissolve the pellet in 36 μl autoclaved water. For bicolor, combine with two labeled cDNAs.

F. Array Hybridization

1. The filtration membrane carrying the 9600 EST PCR product was subjected to 1 × hybridization buffer (4 × SSC, 0.1% N-lauroyl sarcosine, 0.02% SDS, 1% BM blocking reagent (Boehringer Mannheim) for 1.5 hours at 63 ° C.). 5 ml and salmon sperm DNA (Zibco BRL) were previously hybridized to 50 µg / ml. caution. 80 ml of 1 × hybridization buffer is prepared and stored at -20 ° C. Dissolve the buffer at 60 ° C. before use.

2. Adhesive Easy Seal (EasiSeal Attach one side of the square to a clean glass slide and place the pre-hybridized membrane in the center of the square opposite the spots.

3. Mix the probe with 2 μl of poly-d (A) 10 (10 μg / μl), 2 μl of human Cot-1 DNA (10 μg / μl) (Zibco BRL) and 40 μl of 2 × hybridization buffer The final volume reaches 80 μl.

4. Denature the probe mixture at 95 ° C. for 5 minutes and then cool on ice.

5. Seal the filtration membrane with the probe solution in a hybridization bag.

6. Incubate at 95 ° C. for 5 minutes and leave at 63 ° C. for 12-16 hours (overnight).

7. Wash the filter membrane twice with 5 ml of 2 × SSC and 0.1% SDS at room temperature for 5 minutes.

8. Wash at 63 ° C. three times for 15 min each with 5 ml of 0.1 × SSC and 0.1% SDS.

9. The filtration membrane is blocked with 5 ml of 1% blocking reagent containing 2% dextran sulfate for 1 hour at room temperature.

10. 700 × diluted streptavidin-beta-galactosidase (1.38 U / ml, enzyme activity) (Zibco BRL), 10000 × diluted anti-digoxigenin-alkalinephosphatase (2 h at room temperature) Incubate with anti-Digoxigenin-alkalinephosphatase (0.075 U / mL, enzyme activity) (Boehringer Mannheim), 4 mL of polyethylene glycol 8000 (Sigma) and 5 mL of mixture containing 0.3% BSA in 1 × TBS buffer.

11. Wash with 1 × TBS buffer 3 times for 5 minutes each.

12. X-gal substrate solution (1.2 mM X-Gal, 1 mM MgCl ₂ , 3 mM K ₃ Fe (CN) ₆ , in 1 × TBS buffer) by mixing 50 μl 120 mM X-gal with 5 ml of X-Gal substrate buffer. 3 mM K ₄ Fe (CN) ₆ ) is newly prepared. The filtration membrane is slowly shaken and suspended in the X-Gal substrate solution at 37 ° C. for 45 minutes.

13. Wash with 1 × TBS.

14. Double color development: Stain the membrane slowly with 5 ml of Fast red TR / naphthol ASMX substrate (Pierce, Rockford, IL) for 30 minutes at room temperature.

15. Wash with deionized water. The reaction is stopped with 1 × PBS containing 20 mM EDTA for 20 minutes.

16. The filter membrane is air dried.

result.

1. Measurement of Growth Inhibitory Concentration of Herb Extracts in Cell Culture

Since each herbal extract has different cytotoxicity, it is essential to measure the growth inhibitory concentrations of all herbal medicines before treating the cells. Five serially diluted herbal extracts (10, 5, 2, 1, 0.5 mg / ml) were added to 5 x 10 ⁵ / ml of cultured cells and then incubated for 24 hours in a 37 ° C incubator with 5% CO ₂ . It was. The number of viable cells in different concentrations of herbal extracts is shown in Table 18.

Table 18. Viable Cell Counts at Different Concentrations of Herbal Extracts

Experimental concentration Number x 10 ⁵ / ml 10mg / ml 5mg / ml 2mg / ml 1mg / ml 0.5mg / ml No drug High concentration (mg / ml) Low concentration (mg / ml) One Colddiscepcisis mycelium 8.4 11.9 11.5 9.2 9.0 12.4 10 One 2 ST024 4.2 8.7 10 7.5 10 10 10 One 3 ST044 - 5.9 8.4 9.9 9.4 5 0.5 4 ST051 - - - 1.7 5.4 0.5 0.05 5 ST093 - - 1.9 3.8 4.4 0.5 0.05 6 ST117 - 1.6 3.6 4.6 5.8 0.5 0.05 7 ST123 3.4 6.4 8 9.3 7.8 5 0.5 8 ST128 3.5 7.7 7.9 7.7 8.3 5 0.5 9 ST134 2.9 6.1 11.2 9.6 9.8 5 0.5 10 ST237 - - 2.5 6.6 8.7 One 0.1 11 PHY906-303503 - - 4.5 4.9 8.2 One 0.1 12 PHY906-284003 - - 3.8 5.7 7.6 One 0.1

Note: Original cell number was 5 × 10 ⁵ / ml. The cell number increased to 10 x 10 ⁵ / ml after 24 hours of incubation. "-" All indicate dead cells.

The cell counts were doubled after 24 hours of incubation without the addition of herbal extracts. On the other hand, viable cell numbers changed with treatment of different herbal medicines. We chose 50% growth inhibition concentration (IC ₅₀ ) as the high concentration and 1/10 of it as the low concentration. In order to maintain consistency of the Jurkat T cell line, a cell banking system was established. In this cell bank, a total of 100 vials (10 million cells per vial) for the cells were frozen in the -150 ° C freezer.

2. Molecular Classification of Herbal Drugs by Nucleic Acid Microarray Analysis

Analysis of Three Single-Element Herbal Agents. Three single-element herbal drugs, Cordyceps Sinensis Mycelium (CSM), ST024 and ST117, were used to treat cell culture as described in the method section. Gene expression measurements were performed using 13824 cDNA fragment microarrays, each representing distinct human transcripts. For data analysis, high quality gene spot data was selected. The selection is based on the coefficient of variation (CV) of the spot area smaller than 10% or the signal-to-background ratio greater than 2.5. All the data sets were normalized to control cells that did not receive any herbal treatment. Spot strength is rounded up to 10 for century less than 10. Based on the selection criteria, a total of 492 genes with differential expression rates greater than 1.5 were selected for cluster analysis. These data pre-processing procedures were performed by in-house developed "DataExtract" and "Ratio2" programs.

These 492 genes were clustered by means of the mean-linked method. The distance between genes is used as a linear correlation coefficient or similarity coefficient. The cluster analysis programs, Cluster and Treeview, were based on the hierarchical cluster method and were written by Dr Michael Eisen of Stanford University (Eeisen, 1999, Eeisen et al., 1998). The results are shown in FIG. From FIG. 12C, we can clearly identify that three different herbal agents, high and low, are each clustered together. For example, in a cluster schematic, CSM-L is closer to CSM-H and hardly similar to ST024 or ST117. A self-organizing map algorithm based on another clustering algorithm, a non-hierarchical method, produces the same result (data not shown). From the clustering results shown in FIGS. 12A and 13A, several features are noted. (1) Four genes were upregulated by ST117 treatment but downregulated by other herbal treatment (FIG. 12B). (2) 34 genes were downregulated by CSM, but upregulated by other herbal treatments (FIG. 13B). (3) Two genes, one malic enzyme 2 and the other atypical gene (clone ID: 328351), were upregulated by all three herbal treatments (FIG. 13C). (4) In all three herb treatments, 12 genes were significantly induced by high concentration treatment and less by low concentration treatment (FIG. 13D).

Analysis of Two Formulations of Multi-Urea Drugs. Two batches of Huangchintang, low and high concentrations, PHY906-303503 (# 11) and PHY906-284003 (# 12), respectively, were used for cell culture in three independent experiments. Gene expression profiles were obtained with microarrays of 9600 non-redundant cDNA elements. After the data pretreatment procedure as described above, about 5000 genes were selected for subsequent data analysis. Repeated three times for each hub treatment. For data analysis, we use a modified method based on the method published by Slonim et al. (Slonim, 1999). The following algorithm is designed to find candidate marker genes that have large differential expression rates but low deviations in three iterations. We specify P (i) values to count genes (i) having the features described above.

μ: control cells (μ _c) 3-repeated experiments the average expression level in the hub for a non-treated cells (μ _m) or treatment.

σ: Standard deviation of gene expression in three replicates on hub treated cells (σ _m ) or untreated control cells (σ _c ).

We calculated P (i) values for each gene and selected 500 genes with the highest score as candidate genes for cluster analysis (FIG. 14). Each gene value was averaged three or more times. As shown in FIG. 14B, two different concentrations of # 12 were clustered together (12-H & 12-L). The higher concentration of the # 11 formulation is closer to the cluster of the # 12 formulation than the lower concentration of the # 11 formulation. However, all these clusters have similar similarity coefficients (distance between clusters) compared to the schematic diagram shown in FIG. 12. These results indicate that the gene expression profiles of Huangchintang's # 11 and # 12 formulations are similar. The results are correct based on the fact that these two agents are based on the same herbal drug mixture.

Several features are indicated in the gene expression profile illustrated in FIGS. 14 and 15. Average gene expression levels are shown in FIG. 15. Box 1 is downregulated in # 11-L treated cells but surrounds upregulated genes in other agents. These genes include two tRNA synthetase (isoleucine and methionine), RNA polymerase II polypeptide B (clone ID 42020), KIAA0212 gene (clone ID 310497 containing ATP / GTP-binding site motif A) and KIAA0577 ( Clone ID 29263, ATP-dependent RNA helicase). It is interesting to know that three of the six genes were involved in RNA replication. Box 2 encompasses genes upregulated by both # 11 and # 12 treatments. Box 3 was unresponsive by the # 11-L treatment but surrounded a gene that was downregulated by another agent treatment. Box 4 is largely inhibited by low-density herb treatment, but encompasses less suppressed genes by high-density herb treatment. Finally, in Box 1 and Box 3, the gene profile of # 11 treated cells is different from the gene profile produced by the other three treatments. This result is in agreement with the result shown in FIG. 14B.

Combining the data sets of the gene expression profiles of the two formulations of the multi-element herbal drug and the three single-element herbal drugs together, the two features can be seen as examples. The KIAA0212 gene (clone ID 310497 with ATP / GTP-binding site motif A) was greatly induced by both high concentrations of herbal treatment except that it was mildly induced only by the # 11-L and CSM treatments. Two genes, the atypical gene (clone ID 510908) and the proteasome chain 7 precursor, were greatly upregulated in all treatments except downregulated by CSM treatment.

Next we performed the most critical work to cluster analyze gene expression profiles for five different types of herbal pharmaceutical treatments. The data pretreatment procedure was performed as described above and 500 genes were selected for cluster analysis. Hierarchical clustering was performed by the "cluster" program described above. The hierarchical diagram was cut at the location where the distance between clusters was greatest (Romesburg, 1989) and the results are shown in FIG. 16A.

Three single-element herbal medicines of CSM, ST024 and ST117 were clustered together. Among two different batches of multi-element herbal medications, # 12-H, # 12-L and # 11H are clustered together and only # 11L is alone. The results suggest that there is a greater similarity between # 11 and # 12 when compared to CSM, ST024 and ST117.

In order to better classify different herb drugs, the data analysis algorithm is designed to ensure that all individual gene expression levels in the different datasets are zero-average and have a single mutation (Tavazoie et al., 1999; Chen et al., 1999). Improvements were made by standardizing the data set. This yields a modified variable.

: Mean expression level in the data set

: Standard deviation of expression levels in the data set

: Untransformed gene expression level

: Transformed gene expression level

After normalizing the data set, # 11 and # 12 are clustered together as shown in FIG. 16B. ST024 and ST117 are clustered together and the CSM is an independent cluster. Moreover, the clustering also suggests that the CSM is more similar to # 11 and # 12 than ST024 and ST117. Another clustering algorithm, self-organizing map, was performed with the same standardized data set and shows the same results as hierarchical clustering (Figure 16C).

Predictor population to identify # 11 and # 12 hub treated expression profiles. The cluster analysis for # 11 and # 12 above is performed by a hierarchical clustering or self-organizing map method having a data set in which # 11 and # 12 are similar and further contain 500 genes with the largest P (i) values. Indicates that classification is difficult. We modified the algorithm to select genes with larger expression ratios between # 11 and # 12 hub treated cells, but the variation in both hub treated cells was smaller. The T (i) value is defined as follows to record this feature:

μ: mean expression rate in three experiments on # 11 treated cells (μ ₁₁ ) or # 12 treated cells (μ ₁₂ )

σ: standard deviation of expression rate for # 11 treated cells (σ ₁₁ ) or # 12 treated cells (σ ₁₂ )

We calculated the T (i) values for each gene and selected the 50 genes with the highest scores as classification predictors (FIG. 17). 18 genes were upregulated by # 11 treatment and downregulated by # 12 treatment. The remaining genes were upregulated by # 12 treatment and downregulated by # 11 treatment. We then used these classification predictors to classify two test herb formulations based on the modified method described by Golub et al., 1999.

Two different batches of Huangchintang formulations PHY010401 (# 16) and PHY010402 (# 17) were obtained from Sun Ten Pharmaceutical. Co. and used for classification predictor testing. Gene expression profiles of # 16 and # 17 were normalized to the classification predictors by normalizing to the expression profile of untreated control cells. Each predictor g _i is considered a # 11 or # 12 herbal preparation depending on whether the expression level (x _i ) is close to # 11 or # 12. The vote for each gene is given by v _i = | x _i- (μ _m + μ _c ) / 2 |

Where μ: is the average expression rate in three experiments for # 11 (μ ₁₁ ) or # 12 (μ ₁₂ ) hub treated cells.

The average votes V ₁₁ and V ₁₂ were collected from the predictor's correlated predictor genes for # 11 and # 12, respectively. The Prediction Intensity (PS) reflects the limit of victory and was defined as PS = (V ₁₁ -V ₁₂ ) / (V ₁₁ + V ₁₂ ). If PS is greater than zero, it indicates that the herbal formulation is more similar to # 11 and less similar to # 12. Results from analyzes for # 16 and # 17 show that # 16-H is similar to # 11 (PS = 0.1) and that # 16-L, # 17-H, and # 17-L are # 12 (PS = 0.29 respectively). , -0.21 and -0.2). Based on the information of the # 16 and # 17 formulations, this test did not correctly identify # 16-L as being more similar to # 11.

Argument

Characteristic Gene Expression Profiles for Herbalized Cells. Predictor genes were selected from genes expressed differently in two hub treated cells. These genes represent cellular responses to the herbal pharmaceutical treatment. In this study, we identified several genes of interest based on all responses of different drug treatments (FIGS. 13, 15, and 17). These genes are valuable assets for studying cell signaling pathways in response to herbal stimuli and for deciphering the molecular pharmacological mechanisms of herbal medicines.

Classification of herbal medicines by nucleic acid microarray analysis. In summary, a two-stage classification procedure is presented. An initial classification procedure based on a standardized data set and the clustering algorithm is performed followed by a final classification procedure with the classification predictor. All procedures can be integrated into a computer program. In these preliminary studies, all these genes contribute equally for classification. If the data set is large enough, the weights for each gene (or predictor) can be derived from the linear correlation coefficients (Golub et al., 1999, Chen et al., 1999).

The # 11-L was not clustered with # 11-H by meaningful association. A # 16 formulation, similar to # 11, yielded the same results. Whatever clustering algorithm was applied, it is interesting to find that the # 11 and # 16 formulations did not yield the expected results. Even in independent experiments, the results were identical. The reason for the failure will be investigated with detailed information of # 11 and # 16 formulations.

Quality control and analysis in microarray systems. The quality of the acquired microarray data and the choice of statistical analytical method are key factors for obtaining meaningful results. The inventors have found that the variation in the arrangement causes errors in measuring gene expression levels. Based on the data in this report, we found that the highly processed expression profiles for all herb formulations always clustered with low concentration counterparts (FIGS. 16B and 16C), and the two different clustering methods described above for ST117, ST024, and CSM. And Huang Chintang can be classified. Over the past three months, the array quality has improved to have less than 7% CV. We also established standard procedures for measuring the quality of all batches of arrays made in the laboratory. All these experimental findings and improvements in microarray technology mean that they are available for the classification and characterization of Chinese herbal medicines by microarray systems.

References of Example 15

Bertucci-F; Bernard-K; Loriod-B; Chang-YC; Granjeaud-S; Birnbaum-D; Nguyen-C; Peck-K; Jordan-BR (1999) Sensitivity issues in DNA array-based expression measurements and performance of nylon microarrays for small samples Human Mol. Genetics 8 (9): 1715-1722.

Bittner-L, Trent-J, Meltzer-P (1999) Data analysis and integration: of steps and arrows. Nature Genet. 22,213-215

Brown-PO; Botstein-D (1999) Exploring the new world of the genome with DNA microarrays. Nature gentics 21 (1) supplement, 33-37

Chen-JJ; Wu-R; Yang-PC; Huang-JY; Sher-YP; Han-MH; Kao-WC; Lee-PJ; Chiu-TF; Chang-F; Chu-YW; Wu-CW; Peck-K (1998) expression patterns and isolating differentially expressed genes by cDNA microarray system with colorimetry detection. Genomics. 51: 313-24

Chen-Y, Bittner-M, Dougherty-ER (1999) Issue assosiated with microarray data analysis and integration. Information supplementary to article by Michael Bittner, Jeffrey Trent and Paul Meltzer (Nature Genet. 22,213-215)

Duggan-DJ; Bittner-M; Chen-Y; Meltzer-P Trent-JM (1999) Expression profiling using cDNA mircroarrays. Nature genetics 21 (1) supplement, 10-14

Eisen-M (1999) Cluster and Treeview manual. (Rana.stanford.edu/software)

Eisen-M, Spellman-PT, Brown-PO, Botstein-D. (1998) Cluster analysis and display of genewide expression patterns.Proc.Natl.Acad.Sci.USA 99: 14863-14868

Golub-TR, Slonim-DK, Tamayo-P, Huard-C, Gaasenbeek-M, Mesirov-JP, Coller-H, Loh-ML, Downing-JR, Caligiuri-MA, Bloomfield-CD, and Lander-ES (1999 Molecular calssification of cancer: class discovery and class prediction by gene expression monitoring. Science Oct 15: 531-537

Lander-ES (1999) Array of hope. Nature genetics 21 (1) supplement.3-4

Romesburg-HC (1989) Cluster analysis for researchers. Chapter 16: How to make calssifications.P203-216, Krieger Publishing Co. Malabar, Florida, USA

Slonim-DK, Tamayo-P, Mesirov-JP, Golub-TR, Lander-ES (1999) Class prediction and discovery using gene expression data. (www.genome.wi.mit.edu/MPR)

Tavazole-S, Hughes-JD, Campbell-MJ, Cho-RJ, Church-GM. (1999) Systemic determination of genetic network architecture. Nature gentic 22: 281-285

Example 16 Identification of Characteristic Gene Expression Profiles Induced by Herbal Medicines

As described in Example 15, the Chinese herbal medicine, Squat and Licorice combination (Huang Chintang), prescribes diarrhea, relieves seizures, and heats up. Huang Chintang's ingredients are skew, peony, licorice, and date palm fruit. In this study, we studied the gene expression profiles induced by intracellular herbal medicines in mammals using nucleic acid microarray techniques. In order to examine the characteristic gene expression profile induced by Huangchintang, Jurkat T cells were divided into five concentrations (1/2, 1 / 2.5, 1/5, 1/10 and 1/20) of Huangchintang. Dog batches (PHY01040; # 16, PHY010402; # 17, PHY03061; # 18, PHY03062; # 19 and PHY02231; # 20) from Sun Ten Pharmaceutical Company were processed.

Nucleic acid microarrays with two color detection methods were used to measure expression profiles. MRNA extracted from herbal treated cells was labeled digoxigenin and mRNA extracted from non-herb treated cells was labeled biotin. An array of 9600 features was used and the procedures described by Chen et al. (Genomics, 51,313-324,1998) were adopted in this experiment. For data preprocessing, only high data quality array spots were selected. The selection was based on signal-to-background ratios greater than 2.5 and 1.5 × differential expression rates. By this criterion, 1081 genes were further selected for statistical analysis. Non-systematic cluster analysis programs, such as the Genecluster program (Tamayo et al., 1999) developed at the Massachusetts Institute of Technology, were used to classify expression profiles. The GINCluster program is based on the principle of self organizing instruction (SOM). 6 × 4 SOM clustering of gene expression profiles is shown in FIG. 18A. Details of the gene expression profile for the selected cluster are shown in FIG. 18B. In these clusters, clusters 3 and 20 (labeled c3 and c20) were selected because the gene expression level increased at darker hub concentrations. Similarly, clusters 5 and 9 were chosen because the gene expression levels decreased at darker hub concentrations. Cluster 23 collects upregulated genes and cluster 0 collects downregulated genes when the expression level of the genes is compared to untreated cells. These expression profile clusters are further condensed into two main groups A and B. Group A collects genes that are upregulated by herbal treatment and group B collects genes that are downregulated by herbal treatment. Expression profiles in groups A and B form the basis of a characteristic expression data set for herbal formulations. The same procedure was repeated for five different batches of Huangchintang, and 952 genes were selected to establish a characteristic expression profile database of Huangchintang.

As shown in FIG. 19, by the above-described procedure, genes can be classified into groups A, B or independent groups (non-A and non-B) and gene expression profiles are represented as 1,0, -1, respectively. Can be. The number of other gene expression profiles between batch # 1 and batch # 2 is 3 in groups A (genes 6, 7 and 8) and 2 in group B (genes 15 & 16). By the same principle, the number of other gene expression profiles between batch # 1 and batch # 3 is 10 in groups A and B and 11 between batch # 2 and batch # 3. These numbers indicate that batch # 1 and batch # 2 are more similar than batch # 3. This principle was applied to classify five different batches of herbal preparations. The following algorithm was devised to calculate the distance between a pair of herbal formulations (i, j).

(Hamming distance)

Gene X in the i batch of the agent is assigned to group A, B or an independent group.

If X _i <> X _j , δ (X _i , X _j ) = 1

If X _i = X _j , δ (X _i , X _j ) = 0

We calculated all d _ij values between a pair of herbal preparations for cluster analysis. The Kitsch Cluster analysis program is based on the hierarchical clustering principle and was developed by Dr. Joseph Felsenstein of the University of Washington (http://evolution.genetics.washington.edu/phylip.html). Was written. From the Hamming distance table (FIG. 20), it can be clearly seen that the shortest distance lies between batch # 17 and batch # 18 and batch # 17 is similar to # 18. Batch # 16 is also similar to Batch # 17 and # 18, but Batch # 19 is different from the other batches. This result was confirmed by the HPLC method described below.

By HPLC, the chemical compositions of the five batches of the herbal formulations were analyzed. Four major peaks (BG, B, Gly and Pf) in the chromatogram were selected for statistical analysis. Two additional parameters BG + B and BG / B were included in the 6-coordinate radar graph signature as shown in FIG. 21. Each coordinate distance is the combined intensity of a particular chemical in the chromatogram. In general, # 16, # 17 and # 18 were similar to the baiclain, baicalein of Scutellariae Radix within 33.55-36.08; On the other hand, the amount of identical constituents was higher in # 19 and # 20 (42.29 and 44.96, respectively). Similarities of # 16, # 17 and # 18 can be seen from the matching radar coordinates in FIG. 21B.

In order to identify the unknown herbal medication based on the characteristic expression profile constructed as described above, Jurkat T cells were transferred to test sample # 17 at five concentrations to set up the characteristic expression data set for the test apparatus. Was processed. The Hamming distance between the test set and each data set (# 16, # 17, # 18, # 19 and # 20) was calculated in the characteristic expression database and the values were: # 16: 502, # 17: 405, # 18: 402, # 19: 699, # 20: 531. These data show that the test device is most similar to # 17 with the lowest Hamming distance score of 405. This example demonstrates that the present invention directs a method for identifying unknown herbal drugs based on gene expression profiles induced by herbal drugs in mammalian cells. Identification of unknown herbal medications can be deduced by aligning the characteristic gene expression profile with the collection of the characteristic expression profiles of herbal medications in the HBR sequence.

Based on the characteristic expression database, pharmacological mechanisms of marker genes and signature expression profiles for herbal medicines can be studied and inferred to optimize the composition of the composite herbal preparations. For this example, five different batches of Huangchintang formulations (# 16, # 17, # 18, # 19 and # 20) were obtained from Sun Ten Pharmaceutical Co., Ltd. and a characteristic expression profile database was built based on the procedure described above. . For each gene, the consistency of the expression profile in the database was recorded as the variation coefficient (CV value):

μ _i : Average expression rate for #i treated cells

n: number of data sets, in this case n = 5

σ: standard deviation of expression rates for # 16, # 17, # 18, # 19 and # 20

Since the CV reflects the variation of the data, the marker gene for the herbal medicine was selected based on the CV value. The top 50 genes with the lowest CV values were selected. FIG. 22 depicts 25 marker genes with up-regulated signature profile and 25 marker genes with down-regulated signature profile for Huangchintang.

Characteristic expression profile databases can be used to infer the expression profile of individual chemical components in a mixture that is a complex such as a herbal medicine if the amount of the chemical component can be measured about half. In this, the chemical composition of the herbal medicine is measured by high performance liquid chromatography. The combined intensities of the four chemical components in the five batches of the Huangchintang formulation were quantified by HPLC analysis. Gene expression rates for each batch of herbal formulations were calculated by taking the median of expression rates induced by the five concentrations of herbal formulations. The correlation between component and gene expression profiles was quantified by the Pearson correlation coefficient. The Pearson correlation coefficient for gene x and component y is:

n: number of herbal preparations, in this case n = 5

μ _x : mean expression rate in five herbal preparations for gene x

μ _{y is} the mean intensity integrated in the five batch herbal formulations for component y

xi: gene expression rate in #i herbal preparation for gene x

yi: Intensity from #i batch herbal formulation for component y

σ: standard deviation of the expression rate (σ _x ) or integrated intensity (σ _y ) for five drug formulations

Several genes have been identified for each component, whose gene expression levels are highly related to the amount of chemical components in Huang Chintang (| R |> 0.99). For example, the R value between the gene (clone ID: 67185) and glycyrrazine was 0.998 (FIG. 23A). On the other hand, a gene whose expression level increases with decreasing wagonin (WG) (clone ID: 344720) has an R value of -0.997 (FIG. 23B). In addition to the two examples above, the 191 and 170 genes are highly correlated with the individual components having R values> 0.9 and R values <-0.9, respectively. For example, the 17 and 18 genes are positive and negative, respectively, with albiflorin (Af). This example directs a method of profiling gene expression for individual components in a mixture to isolate expression of one component by another component without isolating the components.

References to Example 16

U.S. Patent Documents

Stoughton-Roland and Friend-SH. USP # 5965352: Methods for identifying pathways of drug action

Brown-PO and Shalon-TD USP # 5807522: Methods for fabricating microarraysof biological samples

Lockhart-DJ, Brown-EL, Wong-GG, Chee-MS, and Gingeras-TR. USP # 6040138: Expression monitoring by hybridizaion to high density oligonucleotide arrays

Ladunga-I. USP # 598790: Methods and systems for identification of protein classes

Mahant-S, Shivaling-S, Vivek-G. USP # 5951711: Method and device for determining hamming distance between two multi-bit digital words

Overseas Patent Document

Brown-PO and Shalon-TD EP # 913485A1: Method and apparatus for fabricating microarrays of biological samples

Other publications

Bertucci-F; Bernard-K; Loriod-B; Chang-YC; Granjeaud-S; Birnbaum-D; Nguyen-C; Peck-K; Jordan-BR (1999) Sensitivity issues in DNA array-based expression measurements and performance of nylon microarrays for small samples Human Mol. Genet. 8 (9): 1715-1722.

Bittner-L, Trent-J, Meltzer-P (1999) Data analysis and integration: of steps and arrows. Nature.Genet. 22,213-215

Brown-PO; Bostein-D (1999) Exploring the new world of the genome with DNA microarrays. Nature genet. 21 (1) supplement, 33-37

Chen-JJ; Wu-R; Yang-PC; Huang-JY; Sher-YP; Han-MH; Kao-WC; Lee-PJ; Chiu-TF; Chang-F; Chu-YW; Wu-CW; Peck-K (1998) Expression patterns and isolating differentially expressed genes by cDNA microarray system with colorimetry detection. Genomics. 51: 313-24.

Chen-Y, Bittner-M, Dougherty-ER (1999) Issues assosiated with microarray data analysis and integration. Information supplementary to article by Michael Bittner, Jeffrey Trent and Paul Meltzer, Nature Genet. 22,213-215.

Duggan-DJ; Bittner-M; Chen-Y; Meltzer-P Trent-JM (1999) Expression profiling using cDNA microarrays. Nature genet. 21 (1) supplement, 10-14.

Eisen-M (1999) Cluster and Treeview manual. (Ftp://rana.standford.edu/ software)

Eisen-M, Spellman-PT, Brown-PO, Bostein-D. (1998) Cluster analysis and display of genewide expression patterns.Proc.Natl.Acid.Sci.USA 99: 14863-14868

Golub-TR, Slonim-DK, Tamayo-P, Huard-C, Gaasenbeek-M, Mesirov-JP, Coller-H, Loh-ML, Downing-JR, Caliguri-MA, Bloomfield-CD, and Lander-ES (1999 ) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science Oct 15: 531-537

Lander-ES (1999) Array of hope. Nature genet. 21 (1) supplement, 3-4

Tamayo-P, Slonim-D, Merirov-J, Zhu-Q, Kitareewan-S, Dmitrovsky-E, Lander-ES, and Golub-TR (1999) Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc.Natl.Acad.Sci.USA 99: 2907-2012

Romesburg-HC (1989) Cluster analysis for researchers. Chapter 16: How to make classifications. P203-216, Krieger Publishing Co. Malabar, Florida, USA

Scherf-U, Ross-DT, Waltham-M, Smith-LH, Lee-JK, Tanabe-L, Kohn-KW, Reinhold-WC, Myers-TC, Andrews-DT, Scudiero-DA, Eisen-MB, Sausville- EA, Pommier-Y, Botstein-D, Brown-PO, Weinstein-JN (2000) A gene expression database for the molecular pharmacology of cancer. Nature genet. 24: 236-44.

Slonim-DK, Tamayo-P, Mesirov-JP, Golub-TR, Lander-ES (1999) Class prediction and discovery using gene expression data. (Http://www.genome.wi.mit.edu/MPR)

Tavazole-S, Hughes-JD, Campbell-MJ, Cho-RJ, Church-GM (1999) Systemic determination of genetic network architeture. Nature genet. 22: 281-285

Example 17. Bioreaction of Herbal Compositions and Identification of Signature Genes.

To further investigate the expression profile induced by Huangchintang, Jurkat T cells were prepared by Huangchintang (Taiwan) at 5 concentrations (1/20, 1/10, 1/5, 1 / 2.5, and 1 of IC ₅₀ ). PHY906 # 2 from Sun Ten Pharmaceutical Co., Ltd.). Nucleic acid microarrays with bicolor detectors were used to measure the expression profile. MRNAs extracted from hub treated and unhubed cells were labeled Biotin-16-dUTP and Dig-11-dUTP, respectively. The control group was established using mRNA extracted from untreated cells and labeled with mRNA for biotin and digg at the same rate, respectively. Five sample groups and one control group with different herbal treatment concentrations were used for this study by the procedure described by Chen et al. (1998). For data preprocessing, only array spots of high quality data were selected. The selection criterion was a spot with a signal-to-background ratio greater than 2.5 times and a differential expression rate of 1.5 times or more. By these criteria, 1044 genes were selected for statistical analysis. The gene expression profile of the control group was highly correlated with only 48 genes listed as statistical subregions that exceeded the 2 fold differential expression range (FIG. 25A). For this sample group, many differently expressed genes are evident as illustrated in FIG. 25B. The number of genes with differential expression rates greater than 2 times increases with the concentration of herbal treatment as shown in FIG. 25C. These results demonstrate that the identified differentially expressed genes were truly induced by Huangchintang treatment.

To identify genes specifically induced by PHY906 (signature genes for PHY906), the expression profile is a non-hierarchical cluster analysis program developed by Tamayo et al. GeneCluser). The computer program is based on the self organizing map (SOM) principle and clusters of expression profiles are shown in FIG. 26. The x-axis represents low to high herb concentrations, and the y-axis represents gene expression rates. The signature gene was selected from the expression profile indicating the dosing response to the PHY906 # 2. Induced and suppressed genes were selected from clusters 3 and 4 and clusters 18 and 19, respectively. To identify signature genes for PHY906, another batch of Huangchintang, PHY906 # 3 with the same formula and manufacturing process was performed as described for PHY906 # 2. Induced and suppressed genes commonly found in both batches are shown in FIG. 27.

Similarity record of biological responses by self-organization map (SOM)

To distinguish herbal medicines of similar compositions, a scoring method has been developed, and the score S represents the difference in the biosystem's bioreaction with the two herbal compositions.

S = ΣP _ij W _ij ,

Where P _ij is the number of common genes derived by both Herb A and Herb B in the i and j clusters. For example, SOM clustering results for the expression profile of both PHY906 batches are shown in FIG. 28A. In clusters C13 and C14, the 17 and 25 genes share the same expression profile for both batches of PHY906, respectively. In addition, the expression profiles of the 10 genes induced by PHY906 # 2 were clustered at C13 but at C14 for PHY906 # 3. Therefore, P ₁₃₁₃ = 17, P ₁₄₁₄ = 25, and P ₁₃₁₄ = 10. The weighting factor W _ij describes the distance between the clusters i and j to indicate the similarity of the two expression profile clusters. For C13 and C14, these ten genes respond similarly to PHY906 # 2 and PHY906 # 3 (FIG. 28B). The weighting factor is defined as follows:

W _ij = 1-E _ij / Max (E _ij ), where E _ij is the Euclidean distance between cluster i and cluster j, and the value is normalized by E _ij / Max (E _ij ). When i = j, W _ij is 1, the number decreases as cluster i and cluster j change (FIG. 28C).

Classification of Five Batch Herbal Pharmaceuticals

To test how well the five batches of herbal formulations similar to the above method were sorted, Jurkat T cells were administered at five concentrations (1, 1 / 2.5, 1/5, 1/10, and 1/20 of IC ₅₀ ). Five batches of Huangchintang (PHY01040; # 16, PHY010402; # 17, PHY03061; # 18, PHY03062; # 19, PHY02231; # 20) from Sun Ten Pharmaceutical Co. S _ij values were calculated between herbal preparation pairs in cluster analysis (FIG. 29). The analytical program, Kitsch Cluster, was based on the principle of hierarchical clustering and was written by Dr. Joseph Felsensete (http://evolution.genetics.washington.edu/phylip.html) at the University of Washington. The S value (distance) is plotted (FIG. 29A), and we can clearly see that the shortest distance is between batch # 17 and batch # 18 and batch # 17 is similar to batch # 18. Batch # 16 is also similar to Batch # 17 and Batch # 18, but Batch # 19 is different from the other batches. The results were verified by HPLC analysis.

Characterization of unknown drugs based on expression profile

Tester Sample # 17 at 5 concentrations to identify unknown herbal medications based on the characteristic expression profile database constructed as described above, to establish a characteristic expression data set for the tester. Treated with. In a characteristic expression database, an S value between the test device and each data set (# 16, # 17, # 18, # 19 and # 20) was calculated and the S value was: # 16: 0.78, # 17: 0.85 , # 18: 0.84, # 19: 0.77 and # 20: 0.79. These data show that the test apparatus is most similar to # 17 with the highest value of 0.84. This example shows that the method can be applied to identify unknown herbal medications based on gene expression profiles induced by herbal pharmaceutical compositions in mammalian cells. Identification of unknown herbal medications can be inferred by aligning the characteristic expression profile with the collection of the characteristic expression profile of the agent in the HBR configuration.

The properties of herbs can be described by four properties and five tastes ( Chinese herbal medicines , Ed. Zheng Hua Yen, People's Health Publication, Beijing, China, 1997; Book of Ben Cao Gan Mu by Shi Zeng Li, Ming Dynasty, China). Each of the four hubs of the PHY906 is associated with a different set of herbs with similar properties (see Table 19). Or herbs with similar properties show similar bioreactions. The HBR array can be used to verify or measure correlations with the properties of the hub. This information is useful for creating new herbal preparations.

Table 19: Hubs with PHY906 Properties

Herb Property Drugs with similar properties Scutellariae radix Bitter taste Coptidis Rhizoma, Phellodendri Cortex, Gentianae Radix, Gardeniae Fructus Paeoniae radix Bitter and sour, mildly in favor Canarii Fructus (sweet and sour, mild temper), Potulacae Herba (sour, appetite ), Fraxini Cortex (bitter, appetite ), Sophorae Flos (bitter, mildly appetite ), Bletillae Tuber (bitter, sweet and rough taste, mild Pros) Glycyrrhizae radix Sweetness, mild temper Lycii Fructus, Polygonati Officinalis Rhizoma, Polygonati Rhizoma Zizyphi Fructus Sweetness, warm temper Saccharum Granorum, Juglandis Semen

Property:

4 properties-cold, heat, warm, cold

5 flavors-acrid, bitter, sweet, sour, bland

Example 18 Evaluation of Drugs by HBR Configuration.

As described in Example 16, the constituent herbs of Huang Chintang are Squats, Peony, Licorice and Date palm fruits. The gene expression profile induced by five batches of Huangchintang in mammalian cells was characterized. Prescriptions for Huang Chintang can be defined and characterized as animal or clinical trials. For example, # 17 is used as a standard prescription for Huangchintang based on quality control and other criteria set by Sun Tan Pharmaceutical Company. Bioreactions for # 17 were used to establish the HBR sequence for Huangchintang. Marker genes in the HBR array were selected to evaluate other agents of the Huangchintang composition. Huangqintang testing apparatus will contain the same herbal composition but the component herbs will be grown under various environmental characteristics. The biomechanism of the test device was evaluated by comparing the test device's bioreaction with the marker gene of the standardized HBR sequence.

Moreover, gene expression levels were chosen for evaluation purposes for marker genes (| R |> 0.99) that were highly correlated with the dose of Huangchintang's ingredient herbs. The Huangchintang test apparatus can be assessed by comparing the specific biomarkers or expression levels of the selected marker gene set with the HBR sequence. If the expression level or bioresponse of the selected marker gene is beyond the acceptable mutation region, the amount or characteristic of the component herbs is adjusted or modified to meet the acceptable mutation. The process is repeated until the bioreaction induced by the modified herbal composition is within acceptable variation compared to the standardized HBR arrangement.

Example 19 Prediction of Bioactivity and Therapeutic Applications of Herbal Compositions

According to the identified marker genes for PHY906 (FIG. 27), these genes can be used to predict the biological activity of the herbal composition. For example, the following underlined marker genes of PHY906 have been reported to be involved in the following biological activities and therapeutic effects. The only effective drug for ALL is to inhibit asparagin synthetase due to increased cell apoptosis (Nandy et al., 1998). Somatostatin analogs, which are persistent drugs, treat neurofibroma for cancer growth inhibition because they induce an antiproliferative action mediated by inhibiting G6PD, transketolase , or both. (Boros et al., 1998). Ephrin-A1 is a new melanoma growth factor and is highly expressed during melanoma progression (Easty et al., 1999). Mitogen-activated protein kinase (MAPK) families have been reported to have opposite effects on apoptosis (Dabrowski et al., 2000). Expression of asparagine synthase, transketorase, ephrin-A one and MAPK is inhibited by high concentrations of PHY906 treatment. Downregulation of these genes is associated with cellular apoptosis. Expression of the enzyme argininosuccinate synthetase, cathepsin G, and chemokine RANTES (chemokine RANTES) is greatly induced in the inflammatory mechanism. Inflammation-related genes were inhibited by PHY906 treatment. These literature reports provide the basis for predicting the biological activity or therapeutic effect of herbal compositions.

References to Example 19.

Nandy-P; Periclou-AP; Avramis-VI (1998) The synergism of 6-mercapopurine plus cytosine arabinoside followed by PEG-asparaginase in human leukemia cell lines (CCRF / CEM / 0 and CCRF / CEM / ara-C / 7A) is due to increased cellular apoptosis. Anticancer Research 18: 727-737

Boros-LG; Brandes-JL; Yusuf-FI; Cascante-M; Williams-RD; Schirmer-WJ (1998) Inhibition of the oxidative and nonoxidative pentose phosphate pathways by somatostatin: a possible mechanism of antitumor action. Medical Hypotheses 50: 501-506

Easty-DJ; Hill-SP; Hsu-MY; Fallowfield-ME; Florenes-VA; Herlyn-M; Benett-DC (1999) Up-regulation of ephrin-A1 during melanoma progression.Int. J. Cancer 84: 494-501

Dabrowski-A; Tribillo-I; Dabrowska MI; Wereszczynska-SU; Gabryelewicz-A (2000) Activation of mitogen-activated protein kinases in different models of pancreatic acinar cell damage. Z-Gastroenterol. 38: 469-481

The above detailed description is provided only for clarity of understanding and from which it should be understood that modifications are not unnecessarily limited as would be apparent to those skilled in the art.

While the present invention has been described in connection with specific embodiments, more modifications are possible and the present application is generally within the scope of techniques known or commercially available in accordance with the principles of the invention and in the art to which the invention pertains. It is to be understood that it is intended to embrace all such alterations, uses, or adaptations of the invention, including improvements that may be applied from and to the underlying features set forth above and in the appended claims below.

Included in the detailed description above.

Claims (24)

d) selecting the characterized herbal composition; e) collecting data for two or more markers after exposing the biosystem to the characterized batch herbal composition, wherein one of the markers is iv) making a cell banking system; v) contouring gene expression patterns of cells from said cell banking system before and after exposure to herbal compositions; vi) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step comprising selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition; f) establishing a standardized HB bioreaction array (HBR arrangement) for the herbal composition, comprising storing the marker data of step b) as a standardized HBR arrangement. The method of claim 1 wherein the method is g) repeating steps b) and c) for one or more batches of the herbal composition using two or more markers that are the same or different than those used in step b); h) combining the HBR arrays obtained in steps c) and d); i) analyzing the HBR configuration of step e) combined above to form a standardized HBR configuration for the characterized herbal composition. The method of claim 1 or 2, wherein the characterized herbal composition has at least one known biological reaction. The method of claim 1, wherein the characterized herbal composition is tested for chemical tests, plant parts used, growth conditions for one or more individual herbs of the characterized herbal composition, or for one or more individual herbs of the characterized herbal composition. A method wherein at least one of pre-harvest treatment, post-harvest treatment of one or more individual herbs of the characterized herbal composition, post-harvest treatment of the characterized herbal compositions, and relative ratios of the herbal compositions to the individual herbs are known. 3. The method of claim 1 or 2, wherein said cell banking system comprises a host cell bank and a working cell bank. 6. The method of claim 5, wherein said working cell bank is obtained from a host cell bank. The method of claim 5, wherein the step of contouring the gene expression pattern of the cell from the cell banking system before and after exposure to the herbal composition is performed using cells from the working cell bank. The method of claim 1 or 2, wherein the change in gene expression is determined using nucleic acid microarrays. The method of claim 8, wherein the gene whose expression level is changed by exposure to the herbal composition is based on criteria having a signal to noise rate of at least about 2.5 in nucleic acid microarrays and a change of at least about 1.5 in differentiation expression rates. To be selected. The method of claim 8, wherein data relating to altered expression levels of the gene between about 10 and about 20000 is collected as part of the HBR arrangement. The method of claim 10, wherein the varied expression level data of the gene between about 10 and 1500 is collected as part of the HBR arrangement. a) collecting data for at least two markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step comprising selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition; b) comparing the collected marker data with a standardized HBR sequence for a herbal composition that is the same or substantially the same as that of a batch herbal composition, wherein the standardized HBR sequence comprises containing one marker data for gene expression How to Evaluate Herbal Compositions. a) collecting data for two or more markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step comprising selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition; b) comparing the collected marker data with a standardized HBR arrangement for a herbal composition that is the same or substantially the same as that of the batch herbal composition; c) A method of determining which herbal composition meets a standard description, including determining which herbal composition has marker data similar to that of a standardized HBR arrangement within acceptable levels. The method of claim 13, wherein determining which herbal composition has marker data similar to that of a standardized HBR arrangement within acceptable levels is determined quantitatively or qualitatively. The method of claim 13 or 14, wherein the normalized HBR arrangement comprises an acceptable range of modifications for each marker. a) collecting data for at least two markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step comprising selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition; b) compare the collected marker data with a standardized HBR sequence for a herbal composition that is the same or substantially the same as that of a batch herbal composition, wherein the standardized HBR sequence contains one marker data for gene expression, and also each Includes an acceptable range of modifications to the marker; c) determining whether the herbal composition has marker data that is within acceptable levels of modification for the standardized HBR arrangement; d) Adjusting the components of the herbal composition to meet the standard description for the same or substantially the same herbal composition, including adjusting the components of the herbal composition unless the marker data is an acceptable level of modification for the standardized HBR arrangement. How to. The method of claim 16, wherein the steps a) to d) are repeated until the marker data for the herbal composition is within an acceptable level of modification for the standardized HBR arrangement. a) collecting data for at least two markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step comprising selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition; b) compare the collected marker data with that of a batch herbal composition with standardized HBR arrangements for other herbal compositions, wherein the standardized HBR arrangement contains one marker data for gene expression and also allows for each marker Includes a range of possible modifications; c) determining whether the herbal composition has marker data that is within acceptable levels of modification for the standardized HBR arrangement; d) changing the components of the herbal composition such that the marker data meets a standard description of the other herbal composition comprising changing the components of the herbal composition unless the level of modification is acceptable for a standardized HBR arrangement. The method of claim 18, wherein the steps a) to d) are repeated until the marker data for the herbal composition is within an acceptable level of modification for the standardized HBR arrangement. a) measuring the different responses of two or more markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step of selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition, wherein said set of other response measures Constitutes a hub bioreaction array (HBR array) data set; b) comparing the HBR sequence of the batch herbal composition with a pre-obtained HBR sequence of at least one characterized herbal composition, wherein the pre-obtained HBR sequence contains as one of the marker data for gene expression; c) predicting the biological activity of the herbal composition comprising predicting the biological activity of the batch herbal composition based on the comparison of the HBR configuration made in step b). a) measuring the different responses of two or more markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step of selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition, wherein said set of other response measures Constitutes a hub bioreaction array (HBR array) data set; b) comparing the HBR sequence of the batch herbal composition with a pre-obtained HBR sequence of at least one characterized herbal composition, wherein the pre-obtained HBR sequence contains as one of the marker data for gene expression; c) determining the relevance of the herbal composition to the characterized herbal composition comprising determining the relevance of the herbal composition to the characterized herbal composition based on the comparison of the HBR configuration made in step b). a) measuring the different responses of two or more markers after exposing the biosystem to the batch herbal composition, wherein one of the markers is i) making a cell banking system; ii) contouring gene expression patterns of cells from said cell banking system before and after exposure to the herbal composition; iii) a change in gene expression determined through the use of nucleic acid microarrays prepared by the step of selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition, wherein said set of other response measures Constitutes a hub bioreaction array (HBR array) data set; b) predicting the application of a new therapy comprising predicting the application of a new therapy based on the biological activity of the markers in the predicted HBR arrangement. a) preparing a cell banking system; b) contouring gene expression patterns of cells from said cell banking system before and after exposure to a herbal composition; c) genes whose expression levels have been changed by exposure to the herbal composition are selected as markers and arranged into an HBR arrangement; d) comparing the HBR arrangement made in step c) with a standardized HBR arrangement for similar or modified herbal compositions; e) determine the relative amounts of the individual chemicals in the herbal composition, f) induced by individual chemicals in the herbal composition comprising identifying a change in expression level of the gene as a change in the amount of individual chemicals in the herbal composition compared to the result in step b) above the amount of the individual chemicals To determine a gene expression profile. a) preparing a cell banking system; b) contouring gene expression patterns of cells from said cell banking system before and after exposure to a herbal composition; c) selecting as a marker a gene whose expression level has been changed by exposure to the herbal composition; d) comparing the collected marker data with a standardized HBR arrangement for a substantially identical or modified herbal composition; e) characterizing the chemical components of said herbal composition; f) comparing the identified chemical compositions to identify different levels of individual chemical components in the herbal composition; g) separate the individual chemicals from the complex mixture without extracting the chemicals from the complex mixture, such as the herbal composition, comprising identifying the characterized bioreactions of each chemical in association with the amounts of the different chemical components in relation to the other bioreactions in the HBR configuration. A method of determining a gene expression profile caused by a chemical.