CA2373708A1 - Phytomics: a genomic-based approach to herbal compositions - Google Patents

Abstract

The present invention provides the tools and methodologies necessary to guide the standardization of herbal compositions, to determine which specific components of an herbal composition are responsible for any particular biological activity, to predict the biological activities of a particular herbal composition, to determine the relatedness of herbal compositions, and for the development of improved herbal therapeutics. This invention provides the tools and methodologies for creating, maintaining, improving and utilizing Herbal BioResponse Arrays (HBR Arrays), wherein the HBR Arrays constitute data sets associated with particular herbal compositions. The HBR Arrays of the present invention contain gene expression profiles and may also include information on the plant-related parameters of the herbal constituents, marker information collected following the exposure of a biosystem to the herbal composition, and biological response information collected following the exposure of a biosystem to the herbal composition.

Description

PHYTOMICS: A GENOMIC-BASED APPROACH TO HERBAL COMPOSITIONS
FIELD OF THE INVENTION
This invention relates to herbal compositions. More specifically, this invention provides tools and methodologies for improving the selection, testing, quality control and manufacture of herbal compositions, and to help guide the development of new herbal compositions and identify novel uses of existing herbal compositions.
BACKGROUND OF THE INVENTION
All publications and patent applications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to.be'in~orporated by reference.
Herbal medicine has been in use for centuries by people of Asia and Europe. In the United States (US), herbs have become commercially valuable in the dietary supplement industry as well'as in holistic.riiedicine. Approximately one third of the US
population has tried some form of alternative medicine at least once (Eisenberg et al., 1993, N. Engl. J. Med.
32:246-252).
Botanicals, includiri~ herbsyhave also become a focal point for the identification of new active agents to treat diseases. Active compounds, derived from plant extracts, are of continuing interest to the'pharinaceutical industry. For example, taxol is an antineoplastic drug obtained from the bark of'the western yew tree. It is estimated that approximately 50 percent of the thousands 'af driigs commonly used and prescribed today are either derived from a plant source or contaim~chemical~imitations of a plant compound (Mindell, E.R., 1992, Ecc~l Mindell's Herb Bible;'A~Fireside Book).
Currently, a number of medicinal formulations, food supplements, dietary supplements and thelike contain herbal components or extracts from herbs.
Herbal medicines have been u' sed 'for trieat'ing various diseases of humans and animals in many different countries for a very loiig.period of time (see, e.g., LA. Ross, 1999, Medicinal Plants of the l~orld, Chemical 'Cdhstituehts, Ti°aditio~cal and Modern Medicinal Uses, Humana Press; D. Moloriy,.,1.998;; The American Association of Oriental Medicine's Complete . ~s.. ., Guide to Chinese Herbal Medicine, Berkley Books; Kessler et al., 1996, The Doctor's Complete Guide to Healing,Medicines, Berkley I~ealth/Reference Books);
Mindell, supra).
Herbal Medicines.. There are many branches of herbal medicine around the world, such as Ayurveda, Unani;, Sida and Traditional Chinese Medicine (TCM). While modern Western medicine typicatly,consists~of administering a single. chemical entity capable of intervening. specific biocheinioal pathways, each formula of TCM typically contains hundreds of chemical entities from several herbs which are designed to interact with multiple targets in the body in a coordinated'rrianner. ; ~~lthough empirical practice contributed in a significant way to the herbal composition and,.pre.~cription of these ancient herbal medicines, they are also supported, to a varying degree;,by,a'set of theories which all are distinct from that of modern Western medicine iri terms.t~f~aii"atoxriy,.pharmacology, pathology, diagnosis treatment, etc.
Among the different herbal~iii~edicirle fields; TCM has developed a more complete set of theories over several'centuriesvvhich have been well documented and practiced by local physicians caring for a huge population (>.1.3 billion people) in greater China and in East Asia including~Korea and Japan:' ', . ~ ' '' Westermmedicin~.ge~ierally uses 'purified compounds, either natural or synthetic, mostly directed towards va single physiological target. However, the compositions used in TCM are usually composed of multiple herbs and compounds which are aimed at multiple targets in the body based on viiique and holistic concepts. TCM mainly used processed crude natural products, with'yarious :combiilations and formulations, to treat different conformations resulting iri fewer side effects: The 'great potential of TCM has yet to be realized for the majority of the world°s pebple, ;'~"~ '. ~,~, The herbs iii a typical ~'TCM prescription are assigned roles as the principal herb and the secondary herbs, including.~assis'tant, adjuvant and guiding herbs. The principal herb produces the leading effects in'treatimg~t~i~'cause'or the main symptom of a disease.
An assistant herb helps to strengthen the effect of the principal herb and produces leading effects in the treatment of the accompanying symptofiis.~ There are three types of adjuvant herbs: 1) those that enhance the therapeutic effects of the~priricipal and assistant herbs or treat tertiary symptoms, 2) those that reduce or eliminate the toxicity arid other side effects of the principal and the assistant herbs and 3) those which act dmcoinplementary target tissues not specifically affected by the principal herb. A guiding herb directs 'the effect of other herbs to the affected site and/or coordinates and mediates the~effects~ of the other herbs in the prescription or formulation. In contrast to most of the herbal medicines or supplements that consist of one or more parts of a single plant, the intended~effects of TCM,are directed at multiple tissues.
For example, a well,-known TCM recipe, "Ephedra Decoction" used for treating asthma is composed of ephedra, cinnamon twig, bitter apricot kernel and licorice.
Ephedra, is the principal herb, which expels, cold, induces diaphoresis and facilitates the flow of the Lung Qi to relieve asthma, the main symptoms: , Cinnamon twig, as the assistant herb, enhances ephedra's induction of diaphoresis and warms the Channels to ensure the flow of Yang Qi for reducing headache and pantalgia.. Bitter. apricot kernel, as the adjuvant herb, facilitates the adverse flow of the Lung Qi and strengthens the asthma relief by ephedra.
Licorice as the guiding herb moderates th_e' effects of both ephedra and cinnamon to ensure a homeostasis of the vital Qi. While each of the four,herbs clearly exhibits its respective activity, they ,, complement as well, as suppleri~ent: each other when they are combined. In practice, the principal herb can be prescribed~with one~or more secondary herbs, depending on the symptoms at ~a patient°s ~presetitation .(Prescriptions of Traditional Chinese Medicine, Chapter One, ppl0-1~6, E. Zhang, editor.in Chief, Publishing House, Shanghai University of Traditional Chinese Medicine,1998)., ' . ' ".. '"~ '. , The main theories,'of TAM that guide the treatment of sickness with herbal medicine and other means, such as acupW cture, are 1) the theory of Yin and Yang, 2) the theory of Five Elements, 3) the theoiy of Viscera and Bowel's, 4) the theory of Qi, Blood and Body Fluid, and 5) the theory of Channels and Collaterals.
In TCM, the first~important aspect of making the proper diagnosis is to ascertain whether the disease is Yiri'or'Yarig: .;For example; those patients who have a fever, are thirsty, constipated or~have a rapid~pulse condition are of Yang character. Those individuals who have an aversion to cold, are 'not tliirsty, and diarrhea and a slow pulse conclition are of Yin character. The propeity, ~flavorrand~function ofherbs can also be classified according to Ying and Yang theory. For exaxriple, Herbs'df cold and cool nature belong to Ying, while herbs which are warm and hot~in, nature belong to Yang. Herbs with sour, bitter and salty flavor belong to Ying, while ~herlis~vc~ith pungent, sweet and bland flavor belong to Yang. Herbs with astringent and subsiding function belong to Yin, while herbs with dispersing, ascending and floating function belong to Yang. In TCM, the principles of treatment are based on the predominance or weakness' of Yiri and Yang. Herbs are prescribed according to their property of Ying and Yang and their function for restoring the imbalance of the Ying and Yang. In so doing, the benefit of treatriierit ~is achieved.

According to the theory ~of'Five Elements there are five basic substances that constitute the material world (i.e., wood, fire; earth, metal and water). Tn TC1VI, this theory has been used to explain the physiology and~patliology ~of the human body and to guide clinical diagnosis and treatment. Herbal physicians, have applied the laws of generation, restriction, subjugation and reverse restriction of the five element's to worl~,out many effective and specific treatment regimens, such as reinforcing, earth to generate metal (strengthening the function of the spleen to benefit the lung), replenishing water to nourish wood (nourishing the essence of the kidney to benefit the liver), supporting. earth ~to restrict the wood (supplementing the function of the '.
spleen to treat the hyperactivity of the liver), and strengthening water to control fire (replenishing the essence of the,kidney to treat hyperactivity of the heart).
Specifically, the property of some herbs is assig~ied.to each of the five Elements for the purposes of guiding the -~ .
prescription of a TCM rec'ip~.~, ' . v ' °
In TCM; the internal''organs of the human body are divided into three groups:
five ;.., ° .
Viscera (the Heaxt, the Livef',the Spleen, the Lung and the Kidney), Six Bowels (the Gall Bladder, the Stomach, the,'Iarge Intestine, the Small Intestine, the Urinary Bladder, and the Triple Warmer), the Extraoidinar~ Organs (the Brain, the Medulla, the Bone, the Blood . ;r , Vessel, the Gall Bladder, a'nfalie Uterus). In TCM, the Viscera or the Bowel are not only anatomic units, but are also.'concepts of physiology and pathology about interactions between different organs. For examp'l'e; the.heart also refers to some of the mental functions and influence functions of blood; hair, tongue and skin. Ying-Yang and the Five Elements influence the interactions ainoiig'' th'e'se Viscera, Bowels and Organs. The complexity of interplay of the theories is.u'sed'eto°'e~plain the pathology of diseases to which herbs are prescribed, as discussed~lielow.
The ~prescriptiori of herbal medicine in TCM starts with the diagnosis, which consists of . four main items: interrogation;rimspection, auscintation and olfaction, pulse taking and palpation. During the interrogation phase, much information is gathered, including the characteristics ~of the main .syinptorris: For instance, if the main symptom is characterized by dull pain of epigastric regioii;'vwhich may be relieved by warming and pressing, this suggests the insufficiency of the Spleen-Yang: Soreness and weakness of the loins and la~ees, intolerance of coldness with' cold 'extremities manifests a weakness of the Kidney-Yang.
During inspection, observationsware made for vitality; skin color and the general appearance and the condition of the tongue: ~ Forexample, a pale complexion corresponds internally to the bung of autumn; whose Qi'is diy. This may occur when Yang Qi is lacking and the circulation of Qi and blood is impeded, or when the coldness in the channels and collaterals causes them to contract. ~ .
hi TCM, it is from Qi; ~bloodland body fluid that come energy needed by the Viscera and Bowels, Channels and Collaterals,ltissues and other organs for carrying-out their physiological functions; and'on which the formation and metabolism of Qi, blood and body fluid depend. Prescriptions of TC1VI consider the herbal effects on Qi and blood for treatments.
TCM holds that Channels, Collaterals and their subsidiary parts are distributed over the entire body. It is through them that herbs exert influence on pathological taxgets and achieve the improvement of sickness. For example, ephedra acts on the Channels of the Lung and Urinary Bladder so as to induce sweat for relieving asthma and promoting diuresis. As noted . _.. ,.,a,~:.. ..
above, clinical applications~of acupuncture are also guided by the theory of Channels and Collaterals. ' r In summary, while the nature or property of each herb in TCM may be assigned as Yin or Yang, and to one of the Five Elerilents, they act through Channels and Collaterals and are mediated via Qi, Blood and Fluid tovyield therapeutic effects on taxgets, such as Viscera and Bowels. Pathogenic factors may be~disguised as decoy through the very same systems of Channels and Collaterals ~o adversely affect the functions of Viscera and Bowels and thus cause sickness.
From the foregoing discussion, it is clear that the TCM terminology is as much of a philosophical concept asvan anatomical one. For example, the Heart represents a host of tissues, organs or systems iri the.body that contribute to a function described in TCM. Thus, the concept of the Heart requires a multiple dimension data set to describe each concept of TCM. Once this is accomplished; a molecular holistic medicine can be developed.
U.S. IZegulatory.Proce'ss. :In the US, dietary supplements (such as botanical products, vitamins and minerals, ariiirio''aoids arid tissue extracts) are regulated under the Dietary Supplement Health and Education ~Act~of,1994 (the DSHE Act). This Act removed the ingredients of dietary supplements from regulation as food additives under the Federal Food, Drug, and Cosmetic Act: Iri vaddition, the DSHE Act requires that The Food and Drug Administration (FDA) bearalie burden of proof that a marketed dietary supplement presents a serious or unreasonable risk' under the conditions of use on the label or as commonly consumed. Thus, there .are cuireritly no federal regulations that establish specific criteria for purity, identification and inariufacturing procedixrcs for dietary supplements. In addition, few published papers on herbal quality have resulted from the establishment of the Office of Alternative Medicine by Congress~in'1992 (Angell et al., 1998, N. Engl.
J.~Med. 339:839-841).
At the present time, the~FDA must approve each one of the chemical entities in a drug composition or cocktail,~and then clinical trials must be undertaken so as to obtain separate FDA approval for marketing,tlie drug. This process is extremely tedious and costly. A
molecular holistic medicine may require a less arduous evaluation since the previous use of a particular herbal composition as a,botanical drug permits clinical trials with multiple chemicals at the outset (i.e., clinical trials using the herbal composition or specific components of the herbal composition). Recently, the.FDA has approved the testing of some herbal medicines in clinical trials as botanical drugs (FDA Guidance on Botanical Drugs, April, 1997). While these events represent a positive development for health care in general, it also raises important issues regarding the formulation manufacturing and quality control of herbal medicines and dietary supplements, iricludiiig.the',traditional Chinese medicines.
., .
The multitude ofrelevarit biological responses induced by the multiple chemicals in herbs are not currently available 'and will be increasingly important to support marketing approval by the FDA. ' ~ '', ;
Herbal-based industries are coming under increasing pressure to upgrade their current practices (see, e.g., Angeil etal.; supra). The need to apply scientific testing to the preparation and administration of herbal rriediciries, and food supplements has been highlighted by several recent reports of toxicity resulting froril ingesting herb-based formulations.
For example, one patient who took an herbal-based dietary supplement experienced digitalis toxicity (Slifinan et al., 1998, N. Engl. J. ~Med.'339:806=811). It was subsequently determined that the herb ingredient labeled as plantainvin the supplement was actually contaminated with Digitalis la~cata, an herb known to coritaiii at;least~60 cardiac glycosides. In another instance, an herbal preparation was found to li'e the cause of chronic lead intoxication in a patient (Beigel et al., 1998, N. Engl. J. Med.,33.9:827-830). This is ribt a completely unexpected occurrence since contamination of tradiiional'Asian herbalremedies by lead and other heavy metals is well documented (Woolf et.al'.,:1994, Ann. Intern. IVIed. 121:729-735).
.':
Characterization of BotariicaTs. It.is well known that the genetic identity (e.g., genera, species, cultivar, variety, clone), age of herbal growth, harvest time, the specific plant part utilized, processing nrierhod,~geographical origin, soil type, weather patterns, type and rate of fertilizer, and other grov~thvfactors have a great impact on the particular chemical composition of any particular herb "harvested" from any particular area.

_7_ Increasing numbers of various types of tests have been instituted to assure the consistent quality of herbs 'used irl~medicine and as dietary supplements;
including inspections at the macro- and microscopic levels as' well as a variety of chemical analyses. Recently, high performance liquid chromatography (HPLC) profile of marker molecules in an herbal extract has become one reference standard. However, there are problems with this approach, including that some of the liioactive molecules may not adsorb UV or the visible lights for HPLC detection, and the 'amount of a chemical is not necessarily proportional to its biological potency. For these reasons, herbal manufacturers resort to a practice of mixing raw herbs from different sources to minimize chemical variations.
Mass spectrometry.,(MS) is ~an analytical method for determining the relative masses and relative abundances of components of a beam of ionized molecules or molecular fragments produced from a sample'im a~ high vacuum. ~MS, unlike HPLC, is not optical density-~',, dependent. In practice it 'is~used iri conjunction with HPLC or capillary electrophoresis (CE):
the HPLC separates the 'chemicals and. the MS then can be used to~ identify what they are.
Commercial systems 'are available which integrate MS and HPLC for biological uses. Mass spectrometry is limited to sarriples that'are gaseous or volatile at low pressure, or that can be so rendered by derivatization: .' ' ' These steps are no ~loriger adequate. Recent publications report a greater variation in the quality of herbs by specific 'suppliers, and the difficulty of providing biological equivalence of herbal extracts.. Furthermore, the correlation between safety and efficacy and chemicals in an herb is not well defined in, most cases. Recently, in response to complaints from consumer groups and .regulatory agencies (Federal Register, February 6, 1997, Volume 62, No. 25, Docket No. 96M-0417 cGIVIT'' in Manufacturing, Packing or Holding Dietary Supplements, Proposed Rules), some herbal rnariufacturers have begun to implement Good Manufacturing Practice (GMP) which requires ~stririgerit controls at all levels.
Chemical and spectTOSCOpic.methods~have been used to characterize the components of herbal medicines and food supplements: For'example, three new hederagenin-based acetylated saponins were isolated ~rom.the fruits of Gliricidia sepium using these two methods (Kojima et al., 1998, Phytochemistrv:48('S)$85-888). The botanical sources of Chinese herbal drugs in a number of commercial samples were inferred by comparing the contents of some characteristic constituents which were 'amalyzed with high-performance chromatography (HPLC) or capillary electrophoresis (CE) (Shuerin-Jyi Sheu, .1997, Journal of Food and Drug-Analysis 5(4):285-294). For example, the ratio .of ephedrine/pseudoephedrine was used as a marker to _g_ differentiate EphedYa.ihte~media from other species; total alkaloid contents were used to distinguish between species of Phellodend~on; and the contents of ginsenosides were used to differentiate between species of Panax. However, these methods do not provide a direct . :.. . . , measurement of the effect of the various herbs on the molecular, physiological or morphological responses following human treatment with the herbs.
Using gas chromatography-mass spectrometry and atomic-absorption methods, the California Department of Health Sciences, Food and Drug Branch, recently tested Asian medicines obtained from herbal stores for contaminants (R. J. Ko, 1998, N. En l~. J. Med.
339:847). Of the 260 products they tested, at least 83 (32 percent) contained undeclared pharmaceuticals or heavy metals, and 23 had more than one adulterant. Using high-performance liquid chromatography, gas chromatography, and mass spectrometry, a commercially available combination of eight herbs (PC-SPES), was found to contain estrogenic organic compoyds~(DiPaola et al., 1998, N. En,gl. J. Med. 339:785-791). The researchers concluded that PC _SPES has potent estrogenic activity and that prostate cancer patients that took PC-SPES; could confound the results,of standard therapies and may experience clinically significant adverse effects. Gas chromatography data was also collected for different samples of the traditional Chinese medicine'wei ling xian' and correlated to the antiinflammatory activity of he~sainples (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 )), yTliis study did not provide relevant HBR Array data, such as time course, dose dependent responses control samples to substantiate the differential power of the biomarkers, nor it utilize a.reiterative type. of data construction process to establish a comprehensive database for'characterizing effects of the herbal composition.
Changes in protein levels have also been used to characterize the effects of herbal compositions or specific components of herbs. fiFor example, the production of granulocyte colony-stimulating factor (G~CSF) from peripheral blood mononuclear cells was found to vary depending on which specific Chinese herb was added to the culture (Yamashiki et al., 1992, J_.
Clin. Lab. Immunol. 37(2):83'-90). . Expression of interleukin-1 alpha receptors was markedly up regulated in cultured humamepiderinal keratinocytes treated with Sho-saiko-to, the most commonly.used herbal~medicirle irl Japan (Matsumoto et al., 1997, Jpn. J.
Pharmacol.
73(4):333-336). The expressionof FC gamma 11/111'receptors and complement receptor 3 of macrophages were increased'liy treatment with Toki-shakuyakusan (TSS) (J. C.
Cyong, 1997, Nippon Yakurigaku Zasshi 110(Suppl.: l):87-92). Tetrandrine, an alkaloid isolated from a natural Chinese herbal medicine; inhibited signal-induced NF-kappa B
activation in rat alveolar macrophages (Cheri et a1:,~1997, Biochem. Biophys. Res. Coxrimun.
231(1):99-102).
The herbs Sairei-to, alismatis rhizoma'(Japanese name "Takusha") and hoelen (Japanese name "Bukuryou") inhibited the sy.~thesis and expression of endothelin-1 in rats with anti-glomerular basement membrane nephritis (Hattori et al., 1997, Nippon Jinzo Gakkai Shi 39(2):121-128).
The increase or'decrease in ~mRNA levels has also been used as an indicator of the effect of various herbs and herbal components. Intraperitoneal injection of Qingyangshen (QYS), a traditional Chinese.medicine with antiepileptic properties, and diphenylhydantoin sodium reduced alpha- and,beta-tublin'mRNAs and hippocampal c-fos mRNA
induction during kainic acid-induced chronic, seizures in rats (Guo et al., 1993, J.
Tradit. Chin. Med.
13(4):281-286; Guo et al.,.~19.95, J. Tradit. Chin. Med. 15(4):292-296; Guo et al., 1996, J_.
Tradit. Chin. Med. 16(1):48=51): fireatmerit of cultured human umbilical vein endothelial cells (HUVECs) with the sapomiri astragaloside IV, a component purified from Ast~agalus . ;a.. , membranaceus, decreased plasiiliriogemactivator inhibitor type I (PAI-1) specific mRNA
expression and increased tissue=type plasminogen activator (t-PA) specific mRNA (Zhang et al., 1997, J. Vasc. Res. ,34(4):273-280). One component isolated from the root of Panax ginseng was found to be a potent inducer'of interleukin-8 (IL-8) production by human monocytes and by the huriian riionocytic cell line THP-1, with this induction being accompanied by increased' IL-8 mRNA expression (Sonoda et al., 1998, Immunopharmacolog,~
38:287-294). . ' Recent advances in nucleic acid microarray technology enable massive parallel mining of information on gene expression: This process has been used to study cell cycles, biochemical pathways, genoirie-wide expression in yeast, cell growth, cellular differentiation, cellular responses to a single~chemical,compound, and genetic diseases, including the onset and progression of the diseases ~(M.:Schena. et al., 1998, TIBTECH 16:301).
Because cells respond to the micro=enviibriment,~changes by changing the expression level of specific genes, the identities of genes expressed in a cell determine what the cell is derived of and what biochemical arid regulatory systems are involved, among other things (Brown et al. 1999, Naturea~enet.; 21 (1) supplerrierit:33): Thus,~cellular gene expression profiles portray the origin, the present~differentiatiomof the cell, and the cellular responses to external stimulants.
No researchers to datea if arty, Have attempted to apply these new technologies to study the molecular effects of whole~herbal treatments and supplements.

Some researchers have. attempted to characterize the effects of the major active constituents isolated from selected herbs. For example, treatment of HLTVECs with notoginsenoside Rl (NRl)purifiedfrom Panax notoginsehg, resulted in a dose-and time-dependent increase in TPA synthesis (Zhang et al., 1994, Arteriosclerosis and Thromobosis 14(7):1040-1046). Treatment witli~NRl did not change urokinase-type plasminogen activator and PAI-1 antigen synthesis rior did it effect the deposition of PAI-1 in the extracellular matrix. TPA mRNA increased as~mucli as twofold when HUVECs were treated with NR1, ,.
whereas expression of PAL-1-specific mRNA was not significantly affected by NR1. Since most studies on P. i~otoginsehg have involved its mixture with other herbs, the researchers noted that it was difficult to~assess how their results relate to the situation ih vivo when is used therapeutically in humans ~(Id:, at 1045, second column, first paragraph). In addition, since the researchers only studied brie rnaj'or coriiponent~of the herb, it is not possible to ascertain the molecular effect of the'wliole lierb or the interactions among components of the herb from this study. ,,~ . . . , Dobashi et al. (1995,,.lsTeuroscience Letters 197:235-238) studied the effect of two of the main components of'saiko'agerits~'a Chinese herbal drug used to treat nephrotic syndrome, bronchial asthma and.chronic rheuxriatoid arthritis. Administration of SS-d increased plasma adrenocorticotropin (ACTH) levels; proopiomelanocortin mRNA levels in the anterior pituitary and the CRF~mRNA, level in the rat hypothalamus in a dose dependent rn'anner. In contrast, treatment with SS-a~failed to 'affect the levels of these molecular markers. While this study indicates that administration of SS-d may have an important role in saiko agents-induced CRF release and CRF gene. expression in rat hypothalamus, it fails to address the molecular effect of the herbal medtcatiomas a whole.
I~ojima et al. (1998;"Biol. Pharm. Bull. 4:426-428) describe the utilization of differential display of mRNA to isolate and identify genes transcriptionally regulated in mouse liver by sho-saiko-to, an herbal medicine used for treating various inflammatory diseases in . :. : .~ -.'' . ,w , , Japan. These researchers lirn'itedvheir study to the use of mRNA differential display techniques in investigating ~ttie'~molecular mechanisms of herbal medicine. It also failed to address effects in multiple'orgams of treated animals and did not provide any guidance for quality control, new use, and standardization of effects. In addition, the study failed to analyze the individual components o~,f the' herb and compare the individual results with the results obtained using the whole herlialniiiXture.

Ma Ji et al. (199; Chinese Medical Journal 111(1):17-23) investigated the therapeutic effect of the herb Astragali, yiiembrahaceus on sodium and water retention in rats experiencing aortocaval fistula-caused eXperimental congestive heart failure. Chronic heart failure rats with and without AstYaglia treatment were conipared for changes in various morphological characteristics (e.g., body weight, serum sodium concentration); physiological characteristics (e.g., mean arterial pressure, heart rate, hematocrit and plasma osmolality);
mRNA expression levels (e.g., hypothalamic. arginine vasopressin (AVP),. AVP Vla receptor, renal AVP VZ
receptor, aquaporin-2 (AXP2)) and protein excretion (e.g., plasma atrial monophosphate peptide (ANP) and urinary cyclic guanidino monophosphate (cGMP)). The researchers found that treatment with Astraglia~ improved cardiac and renal functions, partially corrected abnormal mRNA expressions of.the AVP system and AQP2, and improved the renal reaction to ANP. This study did'not ~a~dress using the collected data to guide the development of new formulations or for elucidatingvthe synergistic or other interactions among various herbs in a formula, or validate the differential power of the effects for quality control purposes.
As shown by the abovevreview of relevant scientific articles, molecular-based technology has not been .used to explore and validate cellular and molecular responses in biological 'systems that are treated' or challenged with multiple chemical's at the same time, such as herbal medicines arid TCIVI. ~Furthermore~ these recent advances have not been integrated with other technologies and methods to produce a process for the systematic exploration of biological~effects:of,herbal medicines and TCM.
~' ~" ~' SUMMARY OF THE INVENTION
This invention pro~ides'therools and methodologies for creating, maintaining, improving and utilizing I3erbal BioResponse Arrays (HBR Arrays), wherein the HBR Arrays constitute data sets associ'ate'd°with particulax'herbal compositions.
The HBR Arrays of the present invention may include'~information on'the plant-related parameters of the herbal constituents, marker inforriiati.on collected following the exposure of a biosystem to the herbal composition, and biological resporise~imformation collected following the exposure of a biosystem to the herbal composition..
The present inventiori'provides the tools and methodologies necessary for establishing standardized HBR Array's fox particular herbal compositions, wherein the standardized HBR
Arrays are used as~benchmaxks'by which to evaluate batches of similar or different herbal compositions. The present invention further provides the tools and methodologies necessary to update and maintain the standardized.HBR Arrays. Particular embodiments of the present invention involve iterative~processes whereby data for additional batches of the herbal composition, additional plant=related data, additional marker information, and/or additional BioResponse information isaperiodically added to the standardized HBR Arrays.
Thus, the present invention provides the.tools and methodologies for creating, maintaining, updating and using HBR Arrays on an ongoing basis.
The present invention.provides the tools and methodologies necessary to guide the standardization of herbal compositions; to determine which specific components of herbal compositions are responsible for particular biological activities; to predict the biological activities of herbal compositions; for the development of improved herbal therapeutics; for adjusting or modifying an herbal composition; for measuring the relatedness of different herbal compositions; fox identifying~specihc molecules in the batch herbal composition which retain the desired biological actiyity;~for determining which herbal components of a known herbal composition can be eliminated from 'the known herbal composition while maintaining or improving the desired bio~lo~gical activity of the known herbal composition;
for identifying new uses and previously unkno'wii biological activities for the batch herbal composition; and for using the predicted biologica~~ activity of the batch herbal composition to aid in the design of therapeutics which include herbal components and synthetic chemical drugs, including the design of therapeutics us'irigtlie methods of combinatorial chemistry.
More specifically, the present invention provides methods of establishing standardized I3erbal BioResponse Arrays; (~iBR .Arrays) for herbal compositions, wherein the methods comprise: . - , , .
a) selecting a characterized herbal.composition;
b) exposing a biosystem ~to~~abatch'of the characterized herbal composition and collecting data on two or more markers; wherein one of the markers is a change in gene expression determined through the iiseeo'f a'nucleic acid microarray, produced by the steps comprising:
i) producing a, celfbankirig system;
ii) profiling tlie~geiie'~expressiori pattern of cells from the cell banking system before and after exposure to the herbal composition;
iii) selecting as markers those genes whose expression levels are changed by ' ~eXposure'to~tlie herbal composition;
c) storing the marker~data of~step b)' as~ a standardized HBR array.
The present invention further provides such methods which further comprise exposing a biosysteri~ to one or inore1iatclies of-the lierbal~composition, collecting the data on one or more BioResponses, and adding the collected BioResponse data to the standardized HBR Array for that herbal composition. , The present inventioii~provides methods of evaluating herbal compositions, wherein the methods comprise exposing albiosystem to a batch of the herbal composition and collecting .,.
data on two or more markers; and comparing the collected marker data with a standardized j i HBR Array for the same orb a substantially same herbal composition as that of the batch herbal compositions.
The present invention,provides, a system for predicting the biological activity of an herbal composition comprising: - ry . .
1). a biosystem comprising one or more different types of cells, tissues, organs or i~a vitro assays; 1 ' t, : . . .
2). a batch herbal :coiriposition;
3). two or more ,molecular markers;
4). a means for.'eXposing~the biosystem to the batch herbal composition and measuring the differential respbrises ~of the molecular markers;
S). a computervprocessor, including,memory, for analyzing and storing the differential response measureirients of the molecular markers so as to create an Herbal BioResponse Array (HBR ;!tray) data set for the batch herbal composition;
6). a computer processor; including memory, for comparing the HBR Array of the batch herbal composition to one br more previously-stored HBR Arrays so as to predict the biological activity of the batch herbal composition, wherein the biological activities of the herbal compositions used to ~gerierate the one or more previously-stored HBR
Arrays are known. ' , ...
BRIEF DESCRIPTI~N OF'I'HE DRAWINGS
Fi ure 1. ,Figure 1 'provides°,a schematic of the basic method steps for constructing a Standardized Herbal BioResporise'Array (HBR Array) for any selected herbal composition.
The figure is shown iri its xriost:basic form for ease of understanding. As discussed herein, each of the pathways of thew'sche~natic~ can be done iteratively. Furthermore, any information contained in one box can be 'used to guide decisions regarding gathering information for any other box. In this way, nuriieroits feedback loops are also possible throughout the scheme.
Figure 2. Figure Z~provides ~.~ schematic of the basic method steps for constructing a an Herbal BioResponse Array (HB~t~~Array) for any batch herbal composition and for comparing =14=
this batch IilZB Array to a elected subset of inforination from the Standardized HBR Array.
The figure is shown in its'most.basic form for ease of understanding. As discussed herein, each of the.pathways of the schematic can be done iteratively. Furthermore, any information contained in one box can be.used.to guide decisions regarding gathering information for any other box. In this way, numerous feedback loops are possible throughout the scheme.
Fi ug re 3. Figure 3 provides a schematic of the basic method steps for establishing and using a major data set. The figure-is shown in its most basic form for ease of understanding.
As discussed herein, each of the pathways of the schematic can be done iteratively.
Furthermore, any information contained in one box can be used to guide decisions regarding ... ' . 5~ . .
gathering information for anyother box. In this way, numerous feedback loops are possible throughout the scheme.
Fi ure 4. Western,blbt for various herbal compositions.
A. No herbal composition. ..
B. Huang Qiiig 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/rril). , F. Scute (0.2 mg7inl), w' G. Scute (4 mg%ml). .. .
Figure 5. HPLC for Paeonie lactiflora pallus.
Figure 6. HPLC for Zizipha fructus.
Figure 7. Figure' 7 prpvides a schematic- for establishing a bio-response data set for an herbal composition. The data set is based on differentially expressed gene induced by the herbal medicirie for moreahan three different concentrations in a mammalian cell culture.
~ ' . .
Figure 8. Figure 8' provides a schematic for establishing a characteristic expression profile database or HBR Array for an herbal medicine or a complex herbal preparation.

Fi urg- a 9. Figure' .9'~ provides a schematic for identifying an unl~nown herbal composition. The expression, 'profiles induced by the unknown herbal medicine are aligned with the expression profile database and statistical method is employed to score the possible identities of herbal medicines archived in the database.
,,. .,: ,., . ' .
,.: ;.
Fi ure 1 . Figure 10'~provides a schematic for extracting signature genes for an herbal composition or ~a complex herbal preparation.
Fi rugr a 11. ~ Figure 11''provides a'schematic for extracting signature genes for individual chemical constituents.in axi herbal medicine or a complex herbal preparation.
:, , , ..
Fi urg a 12. Clustered display of gene expression data from cells treated with three types of single-element herbal eXtr'acts (Cordyceps Sinansis Mycelium(CSM), ST024, ST117) with high and low concentrations, (indicated with H and L, respectively).
(A) Cluster analysis Wasperformed by the program "Cluster" (Eisen et al., 1999) with 492 selected genes (see text).
(B) Enlarged image bf genes;up-regulated by ST117 treatment but down-regulated by other herbal extract treatments:. The clone ID and putative gene name are indicated.
(C) The clustering' algorithm~separated CSM, ST024 and ST117 into 3 distinct clusters.
The distance between each cluster as displayed by the hierarchical dendrogram can be viewed as the difference between~'the expression profiles of the three herbal extracts treated cells.
Fi,~ure-13. ~ , . : ' (A) Pseudo=color'encotied display of clustering results as calculated based on the selected 492 genes. The bb~es girl (A) indicate the positions of the three clusters of genes described above. ' -~ ' . . .
(B) Enlarged''image of genes down-regulated by the CSM but up-regulated by the others. ~ ~ ~ ' . ' (C)Genes up=regulated liy all kinds of herbal treatments.
(D) Genes down-regulated by CSM and up-regulated by the others. The IMAGE
clone A~.
ID and putativevgene name' are.iridicated.
. .
Figure 14. Clustered display of expression data from 2 batches of multi-element herbal preparations of the Huaiig~'Chin .Tang (PHY906-303503 (#11) and PHY906-284003 (#12)) treated cells mtn lugn and low concentrations (indicated with H and L, respectively). The data were averaged based on,'three' repeated experiments on three different dates.
Cluster analysis was performed based on the, selected 500 genes (see text). (B) The clustering algorithm separated #11-L, #11H ~andp (#12-H and #12-L) into 3 distinct clusters.
Distance between clusters or resemblance coefficient isindicated by the hierarchical clustering dendrogram.
Figure.l5. Enlarged image~of (A) averaged and (B) individual gene expression levels measured by three independexit experiments. Boxl encloses genes that were down regulated in #11-L treated cells but up regulated in others, Box2 encloses the genes that were up regulated by all the herbal treatments. Box3 enclosed thel genes that showed no response by #11-L
treatment but were dov~n regulated by the others. Box4 encloses the genes highly down regulated by low concentration herbal'treatments but show mild response at high concentration .;;.. ..
herbal treatments.~.The clorie.ID arid putative gene name are indicated beside each gene.
Fi ure 1 . Classification of gene expression profiles in the cells treated by herbal .. ,. . .~, , medicines. Hierarchical clustering of (A) the data sets normalized with the expression data of the untreated control cells and (B) data sets standardized to have zero-mean and unit-variance.
(C) The result of a non-hierarchical;clusteririg by the self organizing maps algorithm.
Figure 17.Candidate class predictors for the classification of herbal medicines based on the gene expression .profiles induced by the medicines. 50 class predictors with their expression profiles for discriminating.#11 and #12 herbal preparations are shown in this figure.
The IMAGE clone ~ID aridvputative~gene name are indicated beside each gene.
Fi ure 18. The gene expression profiles induced by a batch of a complex herbal preparation of five different concentrations.' A 6x4 clustering of expression profiles is shown in (A), and the details of the ~gerie expression profiles for the selected clusters are shown in (B).
Figure 19. Figure 19 illustrates how the expression profiles in Figure 18 are categorized into three different.groups for subsequent hamming distance calculation.
Fi ure 20. Figure~20 shows the analysis.results of gene expression profiles induced by five batches of a complex herbal preparation. The numbers in the table are hamming distance.
The smaller the distance,~tlie more similar are 'the expression profiles.

Fi re 21. Shown im. (A) .is. ~ a table of integrated peak intensities of 4 chemical constituents in HPLC analyses.of five batches of a complex herbal preparation.
Two additional parameters, BG+B and BG/B are introduced to the table and a 6 parameter radial plot is shown in (B) to illustrate that:one' batch is more similar to a second batch #18 than to the other batches by the'HPLC analysis. ~ ~ ' .
Fi re 22. A.display of the signature genes induced by a complex herbal preparation, the Huang Chin Tang, in Jurkat T cells.
Figure 23. Figure 23 illustrates the principle of identifying signature genes induced by individual chemical constituents in a mix of herbal medicines. The signature genes are those whose expression levels correlate with the amount of chemical constituents in the herbal medicine and that the correlation.coefficient is larger than 0.99 or smaller than -0.99. (A) shows that the R value between the, gene and Glycyrrhizin was 0.998, and (B) shows that the IS gene whose, expression levels:iiicrease with the decrease of Wogonin has an R value of-0.997.
' ;
Figure 24. The signature genes induced by the chemical constituent Albiflorin in a complex herbal preparatiori,~ Huax~g Chin Tang, in Jurkat T cell. (A) show the genes that were positively correlated witli~,Albiflorin, and (B) shows the genes that were negatively correlated with Albiflorin.
Figure 25. Correlationvof gene expression profiles to a control group. (A) is the gene expression profile of a controlvgroup, and (B) is the gene expression profile of a sample group.
(C) shows the number of genes with a differential expression ratio having greater than 2-fold increase with concentration of herbal treaterient.
' < ..
Fi ,gore 26. ClustersA:of.,expression profiles clustered by a non-hierarchical analysis program, wherein the,program is~based on a self organizing map (SOM) principle. The X-axis represents the herbal concentration frbm Iow to high and the Y-axis is the gene-expression .,, a:.:
ratio.
Figure 27. Figure 27 shows the induced and repressed genes commonly found in two batches of Huang Chin Tang. , Fi r~ a 28. SOlVf chis'teririg'results for two batches of Huang Chin Tang. (A) shows the f ~ , SOM clustering results for.the'expression profiles of two batches of Huang Chin Tang. (B) shows that ten genes have similarly responded to the two batches, and (C) shows how the weighing factor decreases as~ cluster I and cluster j become more different.
Fi urg a 29. Calculation of S score between pairs of herbal preparations in cluster analysis. (A) is a tabulation,of the scores, and (B) is demonstrates how 5 batches of similar herbal preparations are,related. ~ .~
DETAILED DESCRIPTION OF THE INVENTION
Unless defined otherwise, 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. Although any.inethods arid 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. , Overview 0f the Invention As set forth above, the present invention is directed to tools and methods useful for .: .>;
predicting the biological response of ari herbal composition. More particularly, this invention provides methods of creating Herbal BioResponse Array (HBR Array) databases as well as methods for using such databases to improve the design of effective herbal-based therapeutics.
The goal of the present invention is~ the overall design, creation, improvement and use of HBR
Arrays. for the preparation,'.testing and administration of herbal compositions, and guide development'of new herbal. compositidns and novel uses of existing herbal compositions.
~p:E,, . , .
Phytomics. As used herein, depending on the context in which it is used, "phytomics" refers ~to using' bioinformatics and statistical approaches to address the qualitative and quantitative aspects of the components of herbal compositions or to the actual data bases which are developed for addressing such aspects. , Herlbal BioResponse:~Array. As used herein, an HBR Array constitutes a data set of two or more observations' or'nieasiirements associated with an herbal composition. The HBR
Array may include qualitative 'and quantitative data on the plants in the composition (plant-related data), marker infoririation obtained after exposure of a biosystem to the herbal composition including a dose dependent study, and a database of BioResponse data obtained after exposure of a biosystein to the herbal composition. The data in any particular HBR Array can be statistically analyzed~in'eitlier.2- or 3-dimensional space.

HBR~Arrays may be ~desigmated as batch HBR Arrays and standardized HBR Arrays.
Batch HBR Arrays are aiTays of data associated with specific batches of an herbal composition. Standardized HBR Arrays axe arrays of data associated with a standardized herbal composition. ~ ' ' , Major Data Set. As used herein,~the term °'major data set" refers to the data set which acts as the baseline set of dat'a~by Which various other sets of data are compared or otherwise analyzed for the same or different herbal compositions. Generally, the major data set is created using biotechnological techniques to ascertain some genetic or protein aspect of the herbal compositions. Thus, the major data set will usually, but not always, be based on a genomic or proteomic set of data. For example, nucleic acid microarray results could be the major data set which is used to compare toother, dependent or minor data sets.
Minor 'or Dependent data Set. 'As used herein, the "minor data set" or "dependent data set" refers to one or:rizore~data sets which are used for comparing to the major data set.
Generally, but riot always,y the minor data set will consist of information on an herbal composition which are collected by more traditional methods. For example, the minor, or dependent, data set may consist of a collection of plant-related data obtained by more conventional.means: Example's' 'of plant-related' data include, but axe not limited to, the genus/species of the herbs) iri the herbal composition, the particular plant parts of the herbs) in the composition and the geographic location where the herbs) were located.
Another example of a minor data set might consist of a set of biological responses of a cell, tissue, organ or organism after treatment with' one or more different amounts of the herbal ,~
composition. Exarilples.of suich=biological data or a whole organism may include, but are not limited to, cell toxicity studi~es;~.'eilzyme treatirient studies, growth rates, weight gain or loss, changes in motor skills and changes in mental abilities.
Herb. Technically speaking an herb is 'a~small, non-woody (i.e., fleshy stemmed), annual or perennial seed-b~earing.plant in which all the aerial parts die back at the end of each growing season. Herbs are:valued for their medicinal, savory or aromatic qualities. As the word is more generally used: and: as tie word is used herein, an "herb" refers to any plant or plant part which has a food supplement, medicinal, drug, therapeutic or life-enhancing use.
Thus, as used herein, an k~erli°is.not limited to the botancal definition of an herb but rather to any botanical, plant or plant part used for such purposes, including any plant or plant part of any plant species or subspecies of the IVIetaphyta kingdom, including herbs, shrubs, subshrubs, and trees. Plant parts used.-iri'herbalcompositions include, but are not limited to, seeds, leaves, stems, twigs, branches, buds;' flo~?vers, bulbs, corms, tubers, rhizomes, runners, roots, fruits, cones, bernes, cambium and' bark.
. , , .'' I~erbal Composition.' ,A's used herein, an "herbal composition" refers to any . . . .:
composition which includes herbs, herbal plants or herbal plant parts. Thus, as used herein, an herbal composition is any herbal preparation, including herbal food supplements, herbal medicines, herbal drugs and medical foods. Examples of herbal compositions include, but are not limited to, the following components: a whole plant or a plant part of a single plant species;
whole plants or plant parts 'of multiple plant species; multiple components derived from a single plant species; multiple components derived from multiple plant species;
or any combination of these various. components. For a thorough review of various herbal compositions, see, for example,~Kee.Chang'Huang, The Pharmacology of Chinese Herbs, CRC
Press (1993), herein iricorporat'ed',iri,its'entirety. Representative examples of various herbal compositions are provided 'iiz'the'following paragraphs.
-', Herbal compositions 'vc~hicli include the bark of the willow tree have been used to treat fever since the mid-eighteenth century in England. The active ingredient in willow bark is a bitter glycoside called saliciri;which on hydrolysis yields glucose and salicylic alcohol.
Aspirin (acetylsalicylic'acid)'a~id aspirin-like~drugs (e.g., ibuprofen), all ofwhich are often called nonsteroidal antiiriflammatory-drugs (NSAIDs), are frequently used to treat pain, fever, and inflammation: Meadbwsweet~ is another herb that contains salicylates.
Treatment of arthritic and arthritic-like syiiiptoxris with willow bark or meadowsweet requires the consumption of large quantities of herbalteas made from these plants. The entire Populus species (i.e., poplar tree's arid shrubs) also contains salicylate precursors and poplar-buds have been used in antiinflamniatoiy,~ antipyretic and analgesic medications.
U.S. Patents Have been:issued for herbal compositions used for the treatment of various diseases and other health-rebated problems afflicting humans and animals. For example, U.S.
Patent No. 5,417,979 discloses a composition comprising a mixture of herbs, including species of Stephahia and GlycyYxhiza; as vaell, as their extracts, which is used as an appetite stimulant and for the treatment of pain: ~ ~-F-ierbal compositions which include GlycyYrhiza uralensis have been found useful for treat'img eczema;~psoriasis, pruritis and inflammatory reactions of the skin (U.S. Patent No. 5466;452). U.S. Patent No. 5,595,743 discloses various herbal compositions which include licorice extract (Glycyrrhiza) and siegesbeckia, sophora, stemona and tetrandra herbs used ~fo~~the treatment of various mammalian diseases, including ~,,.: ..
inflammation and rheumatoid; arthritis. Ocular inflammation can be treated with a pharmaceutical composition containing the plant alkaloid tetrandrine (U.S.
Patent No.
5,627,195). ' ' . , U.S. Patent No. 5,683,697 discloses a pharmaceutical composition having anti inflammatory, anti-fever,~expectorant or anti-tussive action, wherein the composition includes plant parts from the, species Melia, Arzgepica, Dendrobium, Impatiens, Citrus, LoYarethus, ° :..
Celosia, Cyhahchum and Glehhaa. , An herbal composition which includes extracts of the roots, rhizomes, andlor vegetation ofAlphinia, Smilax, Tinospora, Tribulus, Withauia and Zihgiber has been found to reduce or~alleviate the symptoms associated with rheumatoid arthritis, osteoarthritis, reactive arthritis and for reducing the production of proinflammatory cytokines (U.S. Patent No. 5,683,698)., , ..
Herbal compositions are available in many forms, including capsules, tablets, or coated tablets; pellets; extracts ontinctures; powders; fresh or dried plants or plant parts; prepared teas; juices; creams and o.intinents; essential oils;.or, as combinations of any of these forms.
Herbal medicines are adniiriist'ered by any one of various methods, including orally, rectally, paxenterally, enterally, trarisdermally, intravenously, via feeding tubes, and topically.
Herbal compositions.emcompassed by the present invention include herbal compositions whichwalso contain non-Herbal components. Examples of such non-herbal components include, but are-riot.lmiited.to, whole insects and insect parts, worms, animal or insect feces, natural or petroleuiYi'oils, carbonate of ammonia, salt of tartar, liquor, water, glycerin, steroids, pharmaceuticals, vitamins, nutrient extracts, whey, salts, and gelatin.
For oral administration, the herbal compositions disclosed may take the form of, for example, tablets or capsules prepared by conventional means in admixture with generally acceptable excipierlts such-~as bindimg.agents (e:g., pregelatinised maize starch, polyvinylpyrrolidone or hydroxypropyl methylcellulose); fillers (e.g., lactose, microcrystalline cellulose or~calciuin phospliale); lubricants (e.g., magnesium stearate, talc or silica);
disintegrarits (e.g.; potato starch,or sodium starch glycolate); or wetting agents (e.g., sodium lauryl sulphate); glidant~s;'arti~'icial~,amd natural flavors and sweeteners;
artificial or natural colors and dyes; and solubilizers.~ The herbal compositions may be additionally formulated to release the active agents in a'tiizie-release manner as is known in the art and as discussed in U.S. Patent Nos. 4,690,825 !amd'S,~O'55,300. The tablets may be coated by methods well known in the art.
Liquid preparations fox oral administration may take the form of, for example, solutions, syrups, suspensions~or slurries (such~as the liquid nutritional supplements described in Mulchandani et al., 1992 LT.S..Patent No. 5,108,767), or they may be presented as a dry product for reconstitution with water or other suitable vehicles before use.
Liquid preparations of folic acid, and other vitamins and minerals may come in the form of a liquid nutritional supplement specifically designed for ESRD patients. Such liquid preparations may be prepared by conventional rrieans with pharmaceutically acceptable additives such as suspending agents (e.g:, sorbitol syrup, methyl cellulose or hydrogenated edible fats);
., emulsifying agents (e.g., alecithin or.acacia); non-aqueous vehicles (e.g., almond oil, oily esters or ethyl alcohol); .preservatives (e.g., methyl or propyl p-hydroxybenzoates or sorbic acid); and -~ , artificial or natural colors and/or sweeteners.
For topical administration, herbal components may be combined in admixture with at least one other ingredient constituting an acceptable, Garner, diluent or excipient in order to provide,a composition, such;as'a,cre~arii, gel, solid, paste, salve, powder, lotion, liquid, aerosol treatment, or the like, wc~hich is rriost.suitable for topical application.
Sterile distilled water alone and simple cream, ointrizent. and gel bases may be employed as carriers of the herbal components. Preservatives and buffers may also be added. The formulation may be applied to a sterile dressing, biodegradable, absorbable patches or dressings for topical application, or to slow release implant systeW s with' a high initial release decaying to slow release.
For a more complete~oveiview and discussion of herbal-based compositions see Earl Mindell, Earl Mindell's Herb°Bible. Simon & Schuster (1992);
Cul~eper°s Complete Herbal, W. Foulsham & Co., Ltd. .(,originally published in the mid 1600's); and, Rodale°s Illustrated Enc,~pedia of Herbs; ~Rodal'e vPress (1987).
Standardized;Herbal'.Composition. ' As used herein, a "standardized herbal composition" or, a "charactdrized~herlial composition" refers to a particular herbal composition wluch is chosen as the standardwlierbal composition for evaluating batch herbal compositions which have the same, similar'br different components as the components of the standardized herbal composition. Sometimes herein al'so.referred to as the "master herbal composition."
Standardized herbal' ~cornpositions: are generally herbal compositions which have been well characterized,and which~d 'emonstrate the desired biological responses in a particular biosystem. Standardi~ed~herbal compositions are'usually standardized by chemical tests well known to one skilledun the'ant~and are properly stored for long term usage and reference. The standardized herbal composition is used to establish a standardized HBR Array based on observations and measurenierits for. the plants (i:e., plant-related data), markers and BioResponses so as to characterize the~herbal composition.

Batch Herbal Composition. ~ As used herein, a "batch herbal composition"
refers to any test herbal composition. which is used to establish a HBR Array based on observations and measurements for the plants and markers so as to characterize the herbal composition.
Sometimes herein also referred to as a ".test" or "batch" herbal composition.
Observations and measurements of Biolzesp~orises riiay,or may not be included. The herbal compositions used to establish the standardized~herbal composition may also be referred to as "batch herbal compositions" until designated.as "standardized herbal compositions."
Batch. ,As used herein,. a "batch" refers to a particular quantity of an herbal composition which can.be identif ed as to some particular attribute so as to distinguish it from any other particular quantity'of that same herbal composition. For example, one batch of an herbal composition may differ from: another batch of that same herbal composition in that one .. .
of the batches was hai-veste'd:'at~ a~different time, or in a different geographical location than the other batch. Other.differences that distinguish particular batches may include, but are not limited to, the following:' 1:) the particular plant part used (e.g., the root of an herb was used in one batch while the leaves of that same herb were used in a different batch);
2) the post-harvest treatment ofthe individual' herbs~~or herbal composition (e.g., one batch may be processed with distilled water while a differentbatch may be processed with Hydrogen Chloride to simulate the acidity of the human.stomach); arid; 3) the relative proportions of the individual herbs in an herbal composition (e.g., one batch may have equal parts by weight or volume of three different herbs while another batch has proportionally more of one herb than the other two).
Biosysterri.' As used lierein~ a "biosystem" refers to any biological entity fox which biological responses may lie obs,etved, or measured. Thus, a biosystem includes, but is not limited to, any cell, fiissueorgan, whole organism or in vitro assay.
Biological Activity'.' As fused herein, the °'biological activity" of an herb refers to the specific biological effect~p~eculiai to an.herbal composition on a given biosystem.
Plant-Related Data: '.As used herein, "Plant-related data" refers to the data collected on the herbal composition including; but not limited to, data about the plants, their growing conditions and the handling ofvtlie plants during and after harvesting. The plant-related data also includes the relative ~iroportioris of the~components in an herbal compositions, wherein the components may be different plant parfs~ different plant species, other non-plant ingredients (e.g., insect parts, chemical~drugs) or ariy combinations of these variables.
Plant-related data whicli~inay'be gathered for an herbal composition includes, but is not limited to, the following:'1) the plaint species (and, if available, the specific plant variety, cultivar, clone, line, etc.) and specific plant parts used in the composition;
2) the geographic origin of the herbs, including the longitude/latitude and elevation; 3) the growth conditions of the herbs, including fertilizer:types and amounts, amounts and times of rainfall and irrigation, average microEinsteins received per day, pesticide usage, including herbicides, insecticides, miticides and fungicides, acid tillage methods; 4) methods and conditions used for processing the herbs, including age/maturity of the herbs, soaking times, drying times, extraction methods ... , . , and grinding methods; and 5) storing methods and conditions for the herbal components and the final herbal composition.
Additionally, the standardized herbal composition may be analyzed chemically.
Chemical characterization may be accomplished by any chemical analysis method generally known by one skilled in,the'art.y~.Examples of applicable chemical analyses include, but are not . , limited to, HPLC, TLC; chemical~fingerprinting, mass spectrophotometer analyses 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 a cell banking system minimizes cell variability,for herbal medicine testing, and is used for all types of cells in nucleic acid microarray studies. . .
Bioinformatics: As~used herein, "bioinformatics" refers to the use and organization of information of biological interest. Bioinformatics covers, among other things, the following:
(1) data acquisition and analysis; (2) database development; (3) integration and links; and (4) fiuther analysis of the,resul'~ing database. Nearly all bioinformatics resources were developed as public domain freeware until the. early.1990s, and much is still available free over the Internet. Some companies.have~developed proprietary databases or analytical software.
Genomic or Genomlcs. As used herein, the term "genomics" refers to the study of genes and their function. ~ Genomics 'emphasizes the integration of basic and applied research in comparative gene mapping;,:molecular cloning, large-scale restriction mapping, and DNA
sequencing and computatidnal~analysis. 'Genetic information is extracted using fundamental techniques, such as DNA; sequencing, protein sequencing and PCR.
,.
Gene function is deterriiirled (1) by analyzing the effects of DNA mutations in genes on normal development.and health of the cell, tissue, organ or organism; (2) by analyzing a variety of signals encoded'viri~tlie DNA sequence; and (3) by studying the proteins produced by a gene or system of related:genes'~

Proteomic or Proteoriiics. ~As~ used herein, the term "proteomics", also called .: .. , . , ; t;: , . :~
"proteome research" or "pheriome", refers to the quantitative protein expression pattern of a genome under defined conditions. As used generally, proteomics refers to methods of high throughput, automated arialysis~using protein biochemistry.
Conducting proteorrie~iesearch in addition.to genome research is necessary for a r. .
number of reasons. First,ahe level of gene expression does not necessarily represent the j' .
amount of active protein in~a cell. Also, the gene sequence does not describe post-tranlsational modifications which are essential for the function and activity of a protein.
In addition, the genome itself does not describe~the dynamic cell processes which alter the protein level either up or down.
Proteome programs seek to characterize all the proteins in a cell, identifying at least part of their amino acid sequence of ~an isolated protein. In general, the proteins are first separatedwsing 2D gels or,HPLC arid then the peptides or proteins are sequenced using high throughput mass spectrometiy.yUsing~a computer, the output of the mass spectrometry can be analyzed so as to' link a gerie,~amd ;the particular protein for which it codes. This overall process is sometimes referred ~to has '"functional genomics". A number of commercial ventures now offer proteomic~services (e.g,,'Pharm~aceutical ProteomicsTM, The ProteinChipTM System from Ciphergen BiosystemPerS,ep~ive Biosystems):
For general information on proteome research, see, for example, J.S. Fruton, 1999, Proteins, Enzymes. Genes:.The Interplay of Chemistry and Biology, Yale Univ.
Pr.; Wilkins et al., 1997, Proteome Research: New Frontiers in Functional Genomics (Principles and Practicel, Springer: Verlag; A.J. .Liuk~ f999, 2-D Proteome Analysis Protocals (Methods in Molecular Biolo~v. ~I12, HumariarPr:.T~amp et al:, 1999, Proteome and Protein AnalXsis, Springer Verlag. , ~ , ..'. , . . . .
Signal Transduetion:vAs' used herein, "signal transduction", also known as cellular signal transductiori, refers to the pathways through which cells receive external signals and transmit, amplify and direct theim internally. Signaling pathways require intercoxmnunicating chains of proteins that. transiriit ~the'signal in a stepwise fashion. Protein kinases often participate in this cascade of peactions, since many signal transductions involve receiving an . :.. .
extracellular chemical signal,~t?vhich triggers~the phosphorylation of cytoplasmic proteins to amplify the signal. .. . , .. ..
Post-translatiorial~Modification. As used herein, "post-translational modification" is a blanket,term used to cover the alterations that:~happen to a protein after it has been synthesized as a primary polypeptide. ~ Such post-translational modifications include, but are not limited to, glycosylatiori,.removal of the N-terminal methionine (or N-formyl methionine), signal peptide removal, aceiylation, foimylation, amino acid modifications, internal cleavage of peptide chains to release smaller proteins or peptides, phosphorylation, and modification of methionine.
Array or Microarray.. As 'used herein, ail "array" or "microarray" refers to a grid system which has each position or probe cell occupied by a defined nucleic acid fragment. The arrays themselves are sometimes referred to as "chips", "biochips", "DNA
chips" or "gene chips". High-density nucleic acid illicroarrays often have thousands of probe cells in a variety of grid styles.
,., .
Once the array is fabricated,' DNA or protein molecules derived from a biosystem are added and some form.of cllerriistry:occurs between the DNA or protein molecules and the array to give some recognition pattern that is particular to that array and biosystem.
Autoradiography of radiolabeled batches is a traditional detection strategy, but other options are available, including fluorescence, coloririletry, and electronic signal transduction.
Markers. As used herein, the term "markers" refers to any biological-based measurement or observatioil~for a particular herbal compositionthat is characteristic of a particular biosystem~ whichvis being exposed~to a particular batch of an herbal composition.
The term "marker" encorilpasses~ both qualitative and ,qualitative measurements and observations of a biosystein: ~ The~riiarker database constitutes a data set which characterizes gene expression patterns in.response to~herbal therapies, wherein the patterns show which genes are turned on; off up; or down in response to specific herbal compositions. Thus, "markers" refers to any'biologically-based measurement or observation whose up-and down-or temporal regulations; or qualitative or quantitative changes of expression levels in a biosystem are used to characterize ~di'fferential biological responses of a biosystem to an herbal composition.
The particular batch' of an herbal composition to which the biosystem is exposed may be an unknown herbal cofripositiori, a'known herbal composition, or a standardized herbal composition. Examples of markers useful in accomplishing the present invention include, but are not limited to, molecular~iiiarkers, cytogenetic markers, biochemical markers or macromolecular markers.' Macroinolecular markers include, but are not limited to, enzymes, polypeptides, peptides; sugars; antifodies, DNA, RNA, proteins (both translational proteins and post-translational proteins),.'nucleic'acids, polysaccharides.

Any marker that satisfies the definition of "marker" herein is appropriate for conducting the present invention. The term "markers" includes related, alternative terms, such as .. ~~w " .., °~r~ ~ w .. .. ' biomarker or genetic marker or gene marker. There may be one or more primary markers along with secondary markers; or a hierarchy of markers for achieving the purposes of increasing the discriminatimg~power of a HBR array. Thus, selected molecular markers may be combined with various other molecular, cytogenetic, biochemical or macromolecular markers to enable an even more accurate, extended HBR Array.
A molecular marker comprisesyone or more microscopic molecules from one or more classes of 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 well known to one skilled in the art. Examples of particularly useful technologies for the characterization of molecular markers include' differential display, reverse transcriptase polymerase chain ~; _ ~,.; . .
reactions (RT-PCR)~, large=scale sequencing of expressed sequence tags (ESTs), serial analysis of gene expression (SAGE); Western immunoblot or 2D, 3D study of proteins, and microarray ., technology. One skilled in he 'art.:of molecular marker technology is familiar with the methods and uses of such technology';('see; e.g:,'Bernard R. Glick and Jack J.
Pasternak, Molecular Biotechnolo~v, Principles and Applications of Recombinant DNA. Second Edition, ASM Press (1998); Mathew R. Walker arid Ralph Rapley, Route Mays in Gene Teclmolo~v, Blackwell Science (1997); Roe et al:, DIsTA Isolatiori~ arid Seduencing, John Wiley &
Sons (1996)James D. Watsori et al.; Recombinant DNA.' Second Edition, Scientific American Books (1992)).
DNA, RNA and p'roteirr isolation and sequencing methods are well known to those skilled in the wart. Exarilples' of such well known techniques can be found in Molecular Cloning;: A Laboratory Maritiaf2rid Edition, Sambrook et al., Cold Spring Harbor N.Y.
(1989); Hanspeter.Saluz and J.vP..~Jost, A LaboratorX Guide to Genomic Seduencin~ The Direct Seduencing~ ofNativeIIJricloned'IDNA (,Biomethods Vol 1), Birkhauser (1988); and B.
Roe et al., DNA Isolation and Se~uencins, Wiley (1996). Examples of conventional molecular biology techniques includes liti't are not limited to, in vitro ligation, restriction endonuclease digestion, PCR, cellular'traris'formation, hybridization, electrophoresis, DNA
sequencing, cell culture, and the like. Specific kits and tools available commercially fox use in the present invention include, but are not limited to; those useful for RNA isolation, PCR
cDNA library construction, retroviral expression libraries, vectors, gene expression analyses, protein antibody purification; cy'totoXicily assays, protein expression and purification, and high-_28_ throughput plasmid purification (see, e.g., CLONTECHnidues product catalog, XIII(3), 1-32 (1998) or www.clontech.com; AtlasTM cDNA Expression Assays product catalog (1998);
SI MA~ product catalog (1997)). ~ .
For discussions, methodologies and applications of oligonucleotide arrays, microarrays, DNA chips or biochips,.see, for,example, U.S. Patent Numbers.5,445,934, 5,605,662, 5,631,134, 5,736,257, 5,74'1,644, 5,744,305, 5,795,714; Schena et al., Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes, Proc. Natl.
Acad. Sci. USA
,; ,.,. , , .
93, 10614-10619 (1996); Del2isi etfal., Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale, Science 278, 680-686 (1997); Wodicka, et al., Genome-wide Expression Monitoring in,Saccharofnyces ce~evisiae, Nature Biotechnoloav 15, (1997); Pardee, Complete Genome Expression Monitoring: The Human Race, Nature Biotechnoloav 15, 1343-134.4 (1997); Schafer et al., DNA variation and the Future of Human Genetics, Nature Biotechnolo~v 16,.3.3-39 (1998); DeRisi et al., Use of a cDNA
Microarray to Analyze Gene Expre'ssiorl~ Patterns in Human Cancer, Nature Genetics 14, 457-460 (1996);
1 S 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., DNA Chips:
An Array of Possibilities,.Nature Biotechnolo~v 16, 27-31 (1998); Schena et al., Microarrays:
Biotechnology's DiscoveryPlatform~for,Functional Genomics, Tibtech 16, 301-306 (1998);
Ramsay, DNA Chips: State-of the-art, Nature Biotechnoloay 16, 40-44 (1998);
Chee et al., Accessing Genetic Information with.High-Density DNA Arrays, cience 274, 610-614 (1996);
and Chen et al., Profilimg.Expression.Patterns and Isolating Differentially Expressed Genes by cDNA Microarray System vvith'.Coloriinetry Detection, Genomics S0, 1-12 (1998); P. Andrew Outinen et al., Character'iz_atio'n of the stress-inducing effects of homocysteine, Biochem. J.
. y 332, 213-221 (.1998); and Gelbert et al., Will genetics really revolutionize the drug discovery process, Curr Opin Biotechilol~8(6), 669-674'(1997).
Other, more specific,, references applicable to the instant invention include, but are not limited to, .those addressing-. the .expression technologies, such as ESTs (see, e.g., Michael R.
Fannon, Gene expressibri~iri normal and disease states - identification of therapeutic targets, TIBTECH 14, 294-298 (1996)); the generation of protein profiles (see, e.g., Robinson et al., A
Tyrosine Kinase Profile o~1'rostate' Carcinoma, Proc. Natl. Acad. Sci. USA 93, (1996)); chemical and spectroscopic methods for identifying components of herbal compositions (Kojima et ~al.°, ~Saponiris from Gliricidia sepium, PhytochemistrX 48(5), 885-888 (1998)); the determination of furictiorial antigens (see, e.g., Aris Persidis, Functional antigenics, Nature Biotechnology 16, 305-307 (1998)); HPLCs (see, e.g., Milton T. W. Hearn (Editor), HPLC of ProteinsYPe~ties~ and Pol~cleotides: Contemporary Topics and Applications (Analytical Technidues in Clinical Chemistry and Laboratory Manual), VCH
Pub. (1991); electrophoresis (see e:g., Westermeier et al., Electrophoresis in Practice: A Guide to Methods and Applications of DNA and Protein Separations, John Wiley & Sons (1997));
~.. ..7.. , . , and cross-reactivity marker assays (see, e.g., Irving Millinan et al., Woodchuck Hepatitis Virus: Experimental Infection and Natural Occurrence, Hepatolog,~ 4(5):817-823 (1984)). The use of structural genomics for solving the structures of all the proteins encoded for in completed genorries, wherein the.methodology includes high-throughput direct structure determinations and computational methods, is discussed by Terry Gaasterland, Structural genomics: Bioinformatics in the driver's seat, Nature Biotechnoloav 16, 625-627. For bioinformatics methodologies, see, for example, Andreas Baxevanis (Editor), Bioinformatics:
A Practical Guide to the AnalXsis of Genes and Proteins, John Wiley & Sons (1998) and Luke ..
Alphey, DNA Seduenciny.,, From Experimental Methods to Bioinformatics (Introduction to Biotechnidues Series,',i;~Sprimger Verlag (1997).
Cytogenetic parameters include, but are not limited to, karyotype analyses (e.g., relative chromosome lengths,; centromere positions, presence or absence of secondary constrictions), ideograms; (i.'e:; a' diagrammatic representation of the karyotype of an organism), the behavior of chromosomes during mitosis and meiosis, chromosome staining and banding patterns, DNA-protein interactions (also known as nuclease protection assays), neutron scattering studies, rolling circles (A.M: Diegelrilan and E.T. Fool, Nucleic Acids Res 26(13):3235-324.'1 (1998); Backert et al:, Mol. Cell. Biol. 16(11):6285-6294 (1996); Skaliter et al., J. Viol. 70(2):11'32-1136 (.1996); A. Fire and S.Q. Xu, Proc. Natl. Acad.
Sci. LTSA
92(10):4641-4645 (1995)),,and~auforadiogr~phy of whole nuclei following incubation with radiolabelled riboriucleatides:' . ' Biochemical pararrieters include, but are not limited to, specific pathway analyses, such as signal transduction, protein: 'synthesis and transport, RNA transcription, cholesterol synthesis and degradation, glucogenesis and glycolysis.
Fingerprinting. As 'used herein, the term °'fingerprinting" as used herein refers to the means of making a characteristio.profile of a substance, particularly an herb, in order to identify it. The term "fingerprint" as used herein refers to the display of the result of the particular means employed. for the fingerprinting.

Examples of the various types of fingerprinting means include, but are not limited to, DNA fingerprinting, protein 'fiiigeiprinting, chemical fingerprinting and footprinting.
DNA fingerprinting; or profiling, refers to a way of making a unique pattern from the DNA of particular biological.~source (e.g., a particular plant, plant species, genus of plant, plant part or plant tissue): 'The DNA~fingeiprint, or profile, can be used to distinguish that particular biological source from a'differerlt biological source: The patterns obtained by analyzing a batch using microarrays;'olig~onucletide arrays, DNA chips or biochips axe also referred to as .'fingerprints". ' .. .
Protein fingerprinting refers, to generating a pattern of proteins in a cell, tissue, organ or organism, such as a plant,. whicli provides a completely characteristic "fingerprint" of that cell, tissue, organ or organism at that' time.
Chemical fingerprinting'refers to the' analysis of the low molecular weight chemicals in ~ ~ ~v:F'.. . ' a cell and the resulting pa,.ttern~ used,to identify a cell, tissue, organ or organism, such as a plant.
The analysis is usually dene using Gas Chromatography (GC), HPLC or mass spectrometry.
Footprinting refers to' a method of finding how two molecules stick together.
In the case of DNA, a protein is bound'to a labeled piece of DNA, and then the DNA is broken down, by enzymes or by chemical~attack. This process produces a "ladder'" of fragments of all sizes.
Where the DNA is protected by the Bound protein it is degraded less, and so the "ladder"
appears fainter. Footprinting;is a,cornmon technique for homing in on where the proteins that regulate gene activity actually'vbind to the DNA.
The means, 'or methods,;used, ~o accomplish each type of fingerprinting are described in detail elsewhere herein. . ; ' ~ ' SioResponses:~ As:iised herein, a'"BioResponse" refers to any observation or ~, measurement of a biologicah'res~ onse of la biosystem following exposure to an herbal composition. Sometimes hereimalso referred to as a "biological effect." A
BioResponse is a qualitative or quantitative data'pomt ;for the biological activity of a particular herbal composition: . Biol~esponse'°data includes~both dosage and temporal information, wherein such information is well known'to~ one skilled in the art of measuring responses of biosystems to various treatments. Thus,~BioResp~onse data includes information on the specific biological response of a specific biosysfein to 'a specific dosage of herbal composition administered in a particular manner for a~speci~c'period of.time.
BioResponses inclucie,.b-ut are not limited to, physiological responses, morphological responses, cognitive responses; motivational responses, autonomic responses and post-translational modification's, ;such as signal transduction measurements. Many herbal compositions demonstrate' more than one BioResponse (see, e.g., Kee Chang Huang, The Pharmacology of Chinese Herbs, CRC'Press (1993)). Some particular BioResponses may be included in more than one of the delineated groups or have aspects or components of the response that encompass iriore than one group. BioResponses applicable to the instant invention are well known to one skilled in the art. The following references are representative of the state of art iwthe field: .Kee .Chang~Huang, The Pharmacology of Chinese Herbs, CRC
Press (1993); Earl Miridell, Earl Mindell's Herb Bible, Simon & Schuster (1992); Goodman &
Gilinan's The Pharmacological~B'asis of Therapeutics~Ninth Edition, Joel G.
Hardman, et. al.
(eds.), McGraw Hill, Health Professions Division (1996); P. J. Bentley, Elements of nharmacology.TA primer on:diu ag ction, Cambridge University Press (1981); P.
T. Marshall and G. IVI. Hughes, Ph, s~~ iology of mammals and other vertebrates, Second Edition, Cambridge University 'Press (1980); Report of~tlie Committee on Infectious Diseases, American Academy of Pediatrics (1991); nut S~chmidt-Nielsen, Animal Ph, siolow: Adaptation and Envirornnent, 5th Edition, Cambridge Univefsity' Press (1997); Elain N. Marieb, Human Anatomy &
Physiology, Addison-Wessley Pub: Co. (1997); William F. Ganong, Review of Medical PhXsiologv~l8th Edl, Appletori ~ Lange (1997); Arthur C. Guyton and John E.
Hall, Textbook of Medical Phs sy iologX, W'. B. Saunders Co. (1995).
A "physiological response" refers to any characteristic related to the physiology, or functioning, of a biosystem,v.Physiological responses on a cellular, tissue or organ level include, but are not limited ~to, temperature, blood flow, rate, pulse rate, oxygen concentration, b'ioelectric potential; pH value, cholesterol levels, infection state (e.g., viral, bacterial) and ion flux. Physiological responses'ori a~wholeorganism basis include gastrointestinal functioning (e.g., ulcers, upset stomach; 'indigestion, heartburn), reproductive tract functioning (e.g., physiologically-based iiilpotence;; uterine cramping, menstrual cramps), excretory functions (e.g., urinary tract problems, kidney' ailments, diarrhea, constipation), blood circulation (e.g., hypertension, heart disorders); oXygenconsumption, skeletal health (e.g., osteoporosis), condition of the cartilage and connective~tissues (e.g., joint pain and inflammation), locomotion, eyesight (e.g.; riiyopia, bluidness), muscle tone (e.g. wasting syndrome, muscle strains), presence or absence~of pain, epidermal and dermal health (e.g., skin irritation, itching, skin wounds), functioning of the endocrine system, caxdiac functioning, nervous coordination, head-related health (e.g.,~he~daches, dizziness), age (e.g., life span, longevity) and respiration (e.g., congestion, respiratory ailrilents).

A "morphological response°' refers to any characteristic related to the morphology, or the form and structure, of a~liiosystem following exposure to an herbal composition.
Morphological responses, regardless of the type of biosystem, include, but are not limited to, size, weight, height, width; color, 'degree of inflammation, general appearance (e.g., opaqueness, transparericy;.paleriess), degree of wetness or dryness, presence or absence of cancerous growths, and the presence or lack of parasites or pests (e.g., mice, lice, fleas).
Morphological responses ;on avwhole organism basis~include, but are not limited to, the amount and location of hair growth (e.g:;v hirsutism, baldness), presence or absence of wrinkles, type and degree of nail and skin growth, .degree of blot clotting, presence or absence of sores or wounds, and presence or absence of hemorrhoids.
A "cognitive response" refers to any characteristic related to the cognitive,'or mental state, of a biosystem folloWimg exposure to an Herbal composition. Cognitive responses include, but are not limited ~toperceivirig~ recognizing, conceiving, judging, memory, reasoning and imagining: ~ ~ ,~ . - ~ v A '°motivatiorial response°''refers to any"characteristic related to the motivation, or induces action, of'a biosyste~rii following exposure to an herbal composition.
Motivational responses include, but are not limited, to, emotion (e.g., cheerfulness), desire, learned drive, particular physiological needs, (e.g:; appetite, sexual drive) or similar impulses that act as incitements to action (e.g., stamina; sex drive).
,., An "autonomic response"'refers to any characteristic related to autonomic responses of a biosystem following exposure to ,an~lierbal composition. Autonomic responses are related to the autonomic nervous systerxi of the biosystem. Examples of autonomic responses include, but art not limited to,Linvoluntary~fiinctio~ung~(e.g., nervousness, panic attacks), or physiological needs (e.g., respir~.'tion, cardiac~rhythm, hormone release, immune responses, insomnia, narcolepsy). ; ~ ' BioResponses~of~cells; tissues; organs and whole organisms treated with various herbal compositions or herbal, cornporlents are well known in the herbal arts. For example, the herbal compositions Sairei-to.(T-J-1:1,4), alismatis~rhizoma (Japanese name'Takusha') and hoelen ~,, (Japanese name 'Bukuryoit') yvere, each found toinhibit the synthesis and expression of endothelin-1 in rats (Hattori e't aZ., Sairei-to may inhibit the synthesis of endothelin-1 in nephritic glomeruli,'ISTippon Jinzo~Gakkai Shi 39(2), 121-128 (1997)).
Interleukin (IL,)-1 alpha production was significantly'promoted~by treatment of cultured human epidermal keratinocytes with the Herbal medicine Sho=saiko-to (Matsmnoto et al., Enhancement of interleukin-1 alpha mediated autocrine growthrof ;cultured human keratinocytes by sho-saiko-to, Jpn J. Pharmacol 73(4), 333-336 (1997). Add~iig'Sho-saiko-to to a culture of peripheral blood mononuclear cells obtained from healthy volunteers resulted in a dose-dependent increase in the production of granulocyte colony=stiriiula'ting~factor (G-CSF) (Yamashiki et al., Herbal medicine "sho-saiko-to" induces in vitro graiiulocyte colony-stimulating factor production on peripheral blood mononuclear cells, J Clin'I~;ab Immunol 37(2), 83-90 (1992)). These researchers concluded that the administration of Sho-saiko-to may be useful for the treatment of chronic liver disease, malignant diseases and acute,infectious diseases where G-CSF is efficacious.
Plasminogen activator inhibitor type l ,(PAI-1)-specific mRNA expression decreased and tissue-type plasminogen activator~(t-P~)-specific mRNA increased after treatment of human umbilical vein endothelial cells (HUVECs) with the saponin astragaloside IV (AS-IV) purified from the Chinese herb Astragalus meiiibrarlaceus' (Zhang et al., Regulation of the fibrinolytic potential of cultured human umbilicalv~invendothelial cells: astragaloside IV down regulates plasminogen activator inhibitor=1 arid~'up regulates tissue-type plasminogen activator expressibn, J Vasc Res 34(4)',273-280'(1997)). One component out of four components isolated from the roots o'f Panax ginseng was found to be a potent inducer of IL-8 production by human monocytes and TI3P-1 cells,,'and this induction was accompanied by increased IL-8 mRNA expression (Sonoda et izl.; Stimulation of interleukin-8 production by acidic polysaccharides from the root of panax ginseng, Immunopharmacolosv 38(3), 287-(1998)). By flow cytometric analysis, the expression of Fc gamma 11/111 receptors and complement receptor 3 ~ (CR3) on macrophages were found to be increased by treatment with the Kampo=herbal medicine~.Toki-shakuyalcusan (TSS) (Cyong, New BRM from kampo-herbal medicine, Ni~pon Yakuri~aku'Zasshi 110 Suppl 1, 87P-92P (1997)). Using computer image analysis, Chen et al. (Image~arialysis for intercellular adhesion molecule-1 expression in l~~RI/lpr mice: effects of Chinese herb medicine, Chung Hua I Hsueh Tsa Chih 75(4), 204-206 (1995)) found that the distribution intensity of intercellular adhesion molecule-1 (ICAM-1), immunoglobulins and C3.were significantly decreased in MRL/lpr mice after treatment with the Chinese herb stragaliri. western blot analysis showed that tetradrine, isolated from a natural Chinese herbal medicine, inhibited signal-induced NF-kappa B
activation in rat ~ alveolar macrophages CChem et al:, Tetrandrine inhibits signal-induced NF-kappa B activation in rat alveolar macrophages; Bioclieril BiophXs°Res Common 231(1), 99-102 (1997)).
Algorltlini. ~ As used Herein, an "algorithm" refers to a step-by-step problem-solving procedure, especially an established, recursive computational procedure with a finite number of steps. Appropriate algorithms for two- and three-dimensional analyses of the plant-related, marker arid BioResponse dafa~sets~ are well known to one skilled in the computational arts.
Such algorithms are useful in constructing the Herbal BioResponse Arrays of the present invention. For general information on algorithiris, see, for example, Jerrod H. Zar, Biostatistical Analwsis, second edition, Prentice Hall (1984); Robert A.
Schowengerdt, Technidues 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 Recog-nition, 31(8):1019-1031 (1998); Berc Rustem, Algorithms for Nonlinear Prog_rammin~ and lVlultinle-Objiective Decisions, Wiley-hlterscience Series in Systems and Optixilization;,~Johm. Whey & Sons (1998); Jeffrey H. Kingston, Algorithms and Data Structures' Design Correctness Analysis International Computer Science series, Addison-Wesley Pub. Co. ,(1997); .Steven S~. Skiena, The Algorithm Desi,~m Manual, Springer Verlag (1997); and Marcel F~''NeutsAl~;orithm Probability A Collection of Problems -(Stochastic Modeling), Cliapmam'& Hall (1995). For information more specific to the application of algorithms togenetic-based data, see, for example, Dan Gusfield, Alg-oritluns on Strings-. Trees, and Seduences: Computer Science and Computational Biology, Cambridge University Press (1997); lVlelanie Mitchell, An Introduction to Genetic Algorithms (Complex Adaptive Svstemsl, MIT Press' (1996); David E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wessley Pub. Co. (1989); Zbigniew Michalewicz, Genetic Alg_orithms~+ Data Structures = Evolution Programs, Springer Verlag (1996); Andre G. Uitterlirideri'arid JamVijg, Two-Dimensional DNA Typing: A
Parallel Approach to Genome Anal°~, Ellis Horwood Series in Molecular Biology, Ellis Horwood Ltd. (1994); 'and Pierre.Baldi. amd Soien Brunak, Bioinformatics: The Machine Learning Approach Adaptive Com~utationvand Machine Learnin~l, MIT Press (1998).
Combinatorial Cheiiiistry: As used herein, "combinatorial chemistry" refers to the numerous technologies used to' create hundreds or thousands of chemical compounds, wherein each of the chemical compounds' differ for one or more features, such as their shape, charge, and/or hydrophobic characteristics: Combinatorial chemistry can be utilized to generate compounds which are cheniic'al vxiiations of herbs or herbal components. Such compounds can be evaluated using 'the methods .of the present invention.
Basic combinatorial. chemistry concepts are well known to one of ordinary skill in the chemical arts and can also' be found in Nicholas K. Terrett, Combinatorial Chemistr;~(Oxford Chemistry. Masters), Oxford Univ: Press (1998); Anthony W. Czarnik and Sheila Hobbs ~35-Dewitt (Editors), A Practicah~Guide to Combinatorial ChemistrX, Amer. Chemical Society (1997); Stephen R. Wilsori,,(Editor) and Anthony W. Czarnik (Contributor), Combinatorial Chemistry: Synthesis and Application, John Wiley & Sons (1997); Eric M. Gordon and James F. Kerwin (Editors), ombinatorial Chemistry and Molecular Diversi in Drug_Discovery, Wiley-Liss (1998); Sluriuel~Cabilly.(Editor),~Combinatorial Peptide Library Protocols (Methods in Molecular Biolo~vl, Human Press (1997); John P. Devlin, High Throushput Screeuns, Marcel Dekker~(1998);'Larry Gold and Joseph Alper, Keeping pace with genomics through combinatorial chemistry;~Nature Biotechnolo~v 15, 297 (1997); Aris Persidis, Combinatorial chemistry,.Nature Biotechnolosv' 16, 691-693 (1998).
' v . . ~ EXAMPLES
Example 1. ~Estalilishing 'a Standardized HBR Array for Selected Herbal ,..;
,,.,:
Compositions. . , , ~ ';, ', :.
' _ 6 v ~ , The basic scheme for establishing a Standardized HBR Array is provided in Figure 1.
Definitions of each com~onerit~ of the schematic are provided above.
Following selection~o~,aWherbal composition of interest, data is collected for various traits associated with the~herb'al'composition, including, but not limited to plant-related characteristics.and inarker'.and.Bio.Response information.
Plant-related data.irichtdes, but is not limited to, the plant species, specific plant parts, geographic origin of the plarits'in the herbal composition, the growth conditions of the plants, the processing methods used to~prepare the herbal components, storage methods and conditions, and various chemical analyses of the herbal composition. Marker information includes qualitative and quantitative 'data for markers collected after exposure of a biosystem to the herbal compost. Applicable iriarl~ers include, but are not limited to, molecular markers, cytogenetic markers bi'ocheinical',markers and macromolecular markers.
BioResponse information includes qualitative' ~ancT quantitative data for biological responses collected after exposure of a biosystem.to,the lierb~al composition:
Each type of data (e:'g.'chemical, marker, BioResponse) cari be obtained using one or more assays'on the same;'similar, substantially similar, or different batches of the herbal composition of interest. Suclivdi'fferent assays can be conducted at the same or different times.
In addition, data can be collected for the same or~different markers at the same or different times. Similarly; BioResp~onse' data can be collected for the same or different biological responses at the same or~different times. ~ Thus, collection of the data for the HBR Array is either collected at one time'or bollected on an on-going basis. Where a biosystem is exposed to an herbal composition so 'as to collect data; information is recorded on the administered dosages of the herbal composition as well as treatment times. BioResponse data may also consist of post-translational modifications, such as measurements of signal transduction.
.,. :~ .E;.
After.collection of tW0 or more types of data (e.g., data for two or more markers and a BioResponse; data for plant-related traits and' data for a BioResponse), the data is analyzed ,; , :; ,, ,. , , , . ~
using algorithms so as to~'create~2- and/or 3-dimensional Herbal BioResponse Arrays.
Various statistical parameters may be calculated for the HBR Array and may become " ~ h~ . ~~'~ , part of the HBR Array data set: .These statistical parameters may include, but are not limited V. 1 to, means, standard deviations, correlation or match (or mismatch) matrices, ratios, regression coefficients, and transformed values (e.g., arcsin percentage transformations of the raw data).
Thus, the HBR Array.may consist of the raw data as well as certain calculations, distributions, graphical presentations and' other data rrianipulations associated with the raw data. Particular examples'of such~informatiowinclude, but are not limited to, digital images, scatter graphs, cluster analyses and large Iscaley gene. expression profiles for marker data.
The total accumulated data and resultant analyses constitute a standardized HBR Array for the particular herbal composition used to establish the HBR Array data set. Due to the :1,~ :.., iterative nature of the process'used to establish and maintain an HBR Array for an herbal ' . . ,.: fw~
composition, such arrays can be viewed as either static at any one point in time or dynamic over time. ~ ' The resulting analyses can identify subsets of the standardized HBR Arrays which axe correlated (positively or negatively) or associated (i.e., showing a general trend) with one or more specific biological activities of any particular herbal composition.
Example 2. Establisliingva Batch HBR Array for. Batch Herbal Compositions.
The basic scheme for establishing a HBR Array for a batch of an herbal composition is provided in Figure 2. ~ Definitions ,of~each component of the schematic are provided above.
The procedure for establishing such an array is the same as that set forth immediately above for the standardized'HBR~'Ai-~ay:' ~~ , Generally, the arribunt~of data collected for a batch HBR Array will be less than that collected to establish''a ~staridardized HBR Array. However, data collected for a batch herbal composition may be added to an,established HBR Array or used to establish a new standardized HBR Array.: v ~ ~, .. ' , Generally, the only data collected for a batch herbal composition is that data which has been found to be highlycorrelated or associated with the desired biological activities of the herbal composition being:tested. '.For example, if it has been determined that a particular subset of plant-related and marker data, is highly correlated to a desired biological activity of a particular herbal composition,(based on the standardized HBR Array data and analyses discussed above), it is only necessary to test the batch herbal composition for that subset of traits in order to determine whether or not the batch has the desired biological activity. By comparing the data obtained-for that subset of traits obtained from the batch (i.e., the batch HBR Array) with the standardised HBR Array.for that particular herbal composition, one skilled in the art can deterinine whether or not that particular batch has the desired biological aCtlvlty. . " ,, . Example 3. Establishing and Using a Major Data Set.
The basic scheme for,establishing and using a major data set for an herbal composition is provided in Figure 3.. 'Defiri~tioiis, of each component of the schematic axe provided above.
The first step, is .the establishment of a major data set for a selected herbal composition or batch herbal coiTipositibn' This, is accomplished by exposing a biosystem to the herbal composition and collecting the resultant marker information which will constitute the major data set. In most, but not instances, the major data set will consist of genomics and/or proteomics data in'the form~o~an axr'ay, such as an array obtained with a DNA
biochip.
Next, the majof data set is' analyzed to see if differential expression/results have been obtained for the tested herbal composition. Differential expression/results are necessary in order to generate meariingful~algorithms in the next step. Examples of such differential expression/results include, but axe not limited to, indications that certain genes are up- or down-regulated in respoMSe to exposure so the herbal composition or that the levels of certain proteins have been iricreasedvor decreased iii response to the exposure.
If no meariingful~ot''usefixl differential expression/results are obtained, then it is necessary to repeat the exposufe.and marker collection step. If it is believed that experimental error lead to the lack of a adequate~~esult the first time then the exposure/data collection step can be repeated with all of the variables the same~as the first time (e.g., same biosystem, same marker set, same experirrierival protocol; etc.): I~owever, it may be necessary to vary the biosystem sampling (e.g.'; type ~of cell utilized, stage of cell growth), use a different marker set and/or change the experirriental protocol in order to get differential expression/result..
Example 4. Using HBI~ Array Information.
The,HBR Array iriforination discussed herein can be used for many different purposes including, but not limited to, 'the following: '1) evaluating the components of an herbal composition; 2) predicting the BioResponse of an herbal composition; 3) determining which marker information is most highly correlated with a particular BioResponse of an herbal composition; 3) determinrig what data set of information (i.e., plant-related data, marker data, and BioResponse data) is iiiost..correlated with a particular BioResporise of an herbal compost;
4) determining which type ofbiosystem is best for evaluating the biological activity of an herbal composition; 5) adjusting or changing the components of a herbal composition so that the I~BR Array of that herbal composition corresponds to a standardized HBR
Array for the same or substantially the same herbal composition; 6) adjusting or changing the components of an herbal composition so that the herbal composition will have the desired biological activity;

7) measuring the relatedness of different herbal compositions; 8) creating and updating standardized HBR Arrays; 9)Aidentifying specific components (e.g., plant parts, proteins, molecules) which retain the 'desired biological activity of an herbal composition; 10) determining which compomerits~ of aWherbal composition can be eliminated while maintaining or improving the desired ~fiological activity of the herbal composition; 11) identifying one or more previously unknown biological activities for an herbal composition; 12) aiding in the design of therapeutics which include Herbal and non-herbal components, such as chemically-synthesized drugs or pharmaceuticals and 13) utilizing the HBR Array information to complement combinatorial oheinistiy rizethods of designing therapeutics. Each of these embodiments of the present invention can be accomplished by one skilled in the applicable art using the methods and tools provided herein.
Exaaazple 5. Qu~ali'ty G~ntrol.
The HBR Array ethnology o~f the present invention is used to correlate or to determine a substantial equivalence'of a~'specific batch of an herbal composition (single herb or multiple ,,:.
herbs of a~ formula) to ~a st'aaidardized, or master, batch of a same or substantial similar herbal composition. The HBR Arrays utilized in this process include the'acceptable range of quantitative variation for ~each~°o~f the biological effects (i.e., BioResponse), and possibly a global score composed of weighted values assigned to each of the biological effects, which may consist of markers from multiple biochemical pathways of a biosystem.
"Data mining" refers.to a'process used to determine or select which subset of biological effects is the rninimuni number of biological effects required in any specific I~BR Array. The information for data mining results from exposing a biosystem (e.g., a cell line) in a dose dependent manner to a standaxdized~herbal composition to establish a standardized HBR
Array. This standardized I~'BR Art~ay can then be compared to various HBR
Arrays established for test herbal, compositions. These test herbal compositions include, but are not limited to, different batches prepared at different dates; different batches prepared from raw herbs collected at different~times; and different batches prepared from raw herbs collected at different locations: . . ' ~ ' Example 6. Improving an Herbal Composition or Identifying New Uses for an Herbal Composition. , ' HBR Arrays are ,generated by exposingFbiosystems to either extracts from individual herbs of a formula, or to extracts from the whole formula, and examining the biological effects of the extracts. The observed biological effects can be from multiple biochemical pathways of a biosystem _and/or from multiple.tissues of an animal, wherein various markers are evaluated for their corresponding qualitative ancUor quantitative changes. The resulting HBR Arrays can be compared to novel~HBR Arrays~or.to similar HBR Arrays from different herbal ,.
compositions or herbal ~coriipositions.prepared by different processes. This procedure is useful for selecting a given set of biological effects and the minimum number of markers required to predict that a given batch herbal composition has the given set of biological effects.
In order to construct:HBR'Axrays, one skilled in the art utilizes various data mining tools including, but are not limited o; statistical analyses, artificial intelligence, and database research on neural work: The statistical methods of choice include, but are not limited to, basic exploratory data analysis ~ (EDA), graphic EDA (such as bushing) and multivariate exploratory techniques (e.g., cluster analysis, discriminating factor analyses, stepwise linear on non-linear regression,classification,tree) ~see~~'e.g., STATISTICATM, software packages from StatSoft, Tulsa, OK 74104; Tel: 918-749-1119; Fax: 918-749-2217; www.statsoft.com).
Data mining tools are usedrto explore large amounts of HBR Array data in search of constructing an HBR Array and~corisistent pattern within, between or among various HBR
Arrays. The procedure consists of exploration, construction of an HBR array, and validation.
This procedure is typically.xepeated iteratively until a robust HBR Array, or standardized HBR
Array, is identified. , ° ~ ~ ' Example '7. Establishing a Standardized HBR Array for Ginseng Recipes.
For the purposes of this exairiple,~standard ginseng is chosen to be Panax Ginsefzg C.A.
Meyer 6115 grown neither in Manchuria or in Korea. The climate for growth is between -10 to +I O°C with an annual rainfall ~of 50-I00 cni (see Huang in The Pharmacology of Chinese Herbs, (1993) pp 2I-45, CRC'.Press, $oca Raton, FL, fully incorporated by reference). Ginseng batches will first be characterized ~by geographic origin, species, plant part (e.g., rhizome, root, leaf skin, seed, bud and flower); growth conditions, processing methods and storage conditions both before and after processing. Verification of chemical content for these batches will be performed by qualitative HPLC analysis for determination of ginsenoside saponins (e.g., Ro, Ral, Ra2, Rbl, Rb2, Rb3, Rc, Rgl, Rg2, Rd, Re, Rf, Rhl, Rh2, NG-R2 and Z-Rl), including S TLC qualitative analysis for lipophilic constituents (see, Elkin et al., Chumg Kuo Yao Li Hsueh Pao (1993) 14: 97-100 and Yoshikawa et al., Yaku~:aku Zasshi (1993) 113:
460-467).
The saponin content of different herbs should be between 2.1 and 20.6% (by weight) depending on the species (see Table 1). These data will then be stored, preferably in the memory of a computer processor, for further manipulation.
Table 1. Saponin Content of Different Ginseng Herbs.*
Species ~ I Total saponins (% by weight) Pahax gihseng C.A. Meyer .. , , , 2.1-4.4%
Panax quiquefolius ' ~ 4.9%
Pahax fZOtogihsehg and Panax japoi2ica ~ 13.6-20.6%
Pahaxjapohica ear. iriajor °-' ' ' I 9.34%
*from Huang~in The Pharmacology~of Chinese Herbs, (1993) page 29, CRC Press, Boca Raton, FL.
Expressionbiomarke~s for standard ginseng (i.e., G11S) include the following:
IL-8, IL-2, GM-CSF, NficB, ICAM-1,, interferon gamma, choline acetyl transferase, trk A, nerve 1S growth factor (Kim et al:,tPlantaMed.(19~98) 64: 110-115; Sonoda et al., Immunopharmacolo~v (1998)'38: 287-294;.Baum et al., Eur J Appl Phvsiol (1997) 76: 16S-169; Iwangawa et al., Free.Radic.Biol.Med (1998) 24: 1256-1268; Rhind et al., Eur J Anal Ph siol (1996) 74: 348-360). Alternatively, for a broader batch size, the 400,000 oligonucleotide group/1.6 cma chip of Affymetrix can be used (U.S. Pat.
No.S,SS6,7S2). The expression biomarkers fo"r standard ginseng will be prepared by nucleic acid microarray technology using eitherphotolithography, mechanical microspotting or ink jet application (see Schena et al., TIBTECIi (1998),16: 301-306). Selected sets of cells will be contacted with standard ginseng for varyiiig periods of time, under varying conditions to generate multiple microarray sets. The microarray set's~will then lie analyzed by hybridization-based expression 2S monitoring of biochemical e~~tracts via deduction of steady state mRNA
levels from fluorescence intensity at each.position on the microarrays (Schena et al., cience (1995) 270:
467-470; Sohena et al., Proc IVatl Acad Sci USA' (1996) 93: 10614-10619;
Lockhart et al. Nat Biotechnol (1996) 14: 167.5-1680;.DeRisi et al., Nat Genet (1996) 14: 457-460;
Heller et al., Proc Natl Acad Sci USA (1997),94: 2150-2155). The array data sets are then input into algorithms to generate statistical expression biomarker values for standard ginseng.
Biochemical biomarkers for standard ginseng include quantitative analysis for increases in cycloheximide sensitive. [3H]-Ieucine incorporation proportional to protein synthesis and [3H]-thymidine incorporation;reflective of mitosis. (see Yamamoto et al., Arzneimittelforschun~
(1977) 27: 1169-1173). For biochemical biomarkers,.bone marrow cells will be contacted with standard ginseng for varying time periods under varying conditions in the presence of [3H]-thymidine (for DNA syntliesis) or in the presence and absence of cycloheximide and [3H]-leucine (for protein synthesis) to :perform multiple quantitative analysis of biochemical biomarkers (i.e., BBM sets).'.The BBM sets are then input into algorithms to generate statistical biochemical biomarker values. for. standard ginseng. Statistical data will then be stored, preferably in the memory of a computer processor, for further manipulation.
Biological resporise,'ofa biosystem (i.e., BioResponses) will be determined using cells and whole animals. For cells,.' ginseng batches will be exposed to specific cell types, including, but not limited to~ fibroblasts, macrophages, monocytes, PMNL, LAK cells, B16-melanoma cells, THP-1 cells and hippocampal neurons at a concentration of 0.5 mg/m1 to 100 mg/ml. For animal treatments,' 0.5-100 mg/kg of ginseng herbal extract will be administered orally, by intraperitorieal irijectiomor subcutaneous injection.
To determine a biological response of a biosystem to standardized ginseng, human ovarian cancer cells willrbe inoculated into nude mice, which results in the formation of palpable tumors. After tumor' formation the mice will be treated by co-administration of cis-diamminecichloroplatinum arid ~staiidard. ginseng. Mice will be examined for tumor growth inhibition, increase iii sv=vival time and lowered adverse side-effects on hematocrit values and body weight (Nakata~et'al.;~ J~h J Cancer Res (1998) 89:733-740). The assay will be repeated using various concentrations "of stamdard ginseng to generate measures of central tendency, dispersion and variability for.eacli variable.
The data collected will then~be subj ected ~to multidimensional analysis to generate multivariant normal distribution sets as a means of determining a baseline correlation between biological activity and standard ginseng (see Zar, J. H., in Biostatistical Anal~~sis. 2nd ed.
(I984), pp 328-360, ~PreriticeTIaII, Eriglewood Cliffs, NJ). A second independent determination of a Iiiologicah response.of a biosystem to standard ginseng will be the effect of standard ginseng ori~physicalvperforinance during exercise. Rats will be treated for 4 days with standard ginseng at various" concentrations (between 0.5-100 mg/kg/day) and animals will be tested for increasedplasma free~fatty acid level and maintenance of glucose level during exercise at approximately'70% V02max (see Wang et al., Planta Med (1998) 64130-133).
The data generated will be:/collected and then subjected to multidimensional analysis to generate multivariant~ribrmal distribution sets as a means of.detertizining a baseline correlation between biological activity and standard ginseng (see Zax, J. H., in Biostatistical Anal, sis, 2nd ed. (1984), pp 328-360, Preritice Hall,~Englewood Cliffs, NJ, Herein, fully incorporated by reference). The distribution sets for each BioResponse are then put~into algorithms to generate statistical values fox standard ginseng: Statistical data will then be stored, preferably in a memory of a computer processor, for further manipulation.
Each of these steps (a. e., chemical analysis, generation of biomarker information and determination of responsess of a biosystem) is reiterated to generate a large database of statistical values. These values are compiled and input into an algorithm to generate 2- and 3-dimensional Herbal Response' Arrays (HBR Array) for standardized ginseng.
Through reiteration, the resitltirig arrays (a.,e:~'Standardized Arrays) display the highest correlation between composition(iric'ludirig'~growth conditions), biomarker information and biological response for standardized ,gin5erig.', ,By determining two or more known associated variables for composition and biomarkar information values via display on an HBR Array for a test batch, the values for biological response variables can be predicted for the test batch by comparing test values'agairist Standardized HBR Array values for standardized ginseng. The resulting prediction will be used. to evaluate the quality of a given ginseng batch without necessitating the use.of amobserve~.biological-response of a biosystem (see Example 2).
Example 8. Evaluation of a Selected. Herbal Composition of Ginseng Using a Subset of Variables Correlated with a Specific Biological Response.
To evaluate tf~e quality.~ofa~ est batch herbal composition, data is first collected concerning the plant-related,parameters for the~herbs in the selected herbal composition (e.g., plant species, plant parts, ge'ographic'origin, growth conditions, processing methods and storage conditions). The selected herbal composition is then manipulated such that chemical analysis can be performed t'o' determine the chemical content of the herb (see Elkin et al., Chumg Kuo Yao Li I=Isueli~Pab (1993)=14: 97-100 and Yoshikawa et al., Yaku~;aku Zasshi (1993) 113: 460-467). Previously obtained ginseng data has demonstrated a strong correlation between oxygen consumption during aerobic exercise performance and the presence of a subset of saponin components; especially Rg1 and Rbl (Wang et al., Planta Med (1998) 64:
130-133).
The test batch,is then exposed to test cells including, but not limited to, fibroblasts, macrophages, monocytes; PMNL, LAK cells, B16-F10 melanoma cells, THP-1 cells and hippocampalneurons at a concentration of 0.5 mg/m1 to 100 mg/ml to determine expression biomarker values. . mRNA is isolated from exposed cells which is subsequently manipulated to serve as a substrate for hybridization-based expression moutoring 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 enet (1996) 14: 457-460; Heller et al., Proc Natl~Acad S~ci USA (1997) 94: 2150-2155). Previously obtained ginseng data has demoristraYed a~strong correlation between oxygen consumption during aerobic exercise performanceand the induction of the expression biomarkers IL-8, IL-2 and Interferon gamma in test cellsv(Venkatramawet al.; Med Sci Sports Exerc (1997) 29: 333-344 and Wang et al., Planta Med (1,998).64: 130-133). For biochemical biomarkers, rat bone marrow cells will then be exliosed to the test batch and assayed for [3HJ-thymidine incorporation reflective of~mitosis. Previously obtained ginseng data has demonstrated that Rbl and Rgl show a strong correlation with DNA synthesis in rat bone marrow cells (Yamamoto et al., Arzneiriiittelforschuns (1'978) 28: 2238-2241).
After reiterative~aiialysis,'°data from each assay will be input into an algorithm to generate a test HBR array~-for the;selected herbal composition based on the enumerated plant-related data, including chemical analyses, and data concerning the subset of biomarkers. The quality of a test batch wVill lie deterrizined by comparing test HBR and standard ginseng Standardized HBR Array variables directed toward analysis of the above observations and subsets, wherein the demoris~ration bf the induction of IL-2, IL-8 and INF
gamma mRNA i~a.
vitro and an increase in [3H],=thyinidirie. incorporation in rat bone marrow cells (including data collected on growth conditions, origin, and verification of the saponins Rgl and Rbl) is predictive cif an equivalent Bioltesponse effect of the test batch on oxygen consumption as that exhibited by standard ginseng. Based~on this procedure it can be determined Whether or not the test batch is of a similar~'or different quality than that of the standard for the given biological response or biological response of interest.

=44-Example 9. Establishing a Standardized HBR Array for Huang Ling (HL) ,:
Recipes. ' ' . , .
f . ~,,~t...~.W
For the purposes of this example, standard huang ling (HL) is chosen to be Coptis chiyaesis France, from southwest Asia, wherein growth conditions are well known to one skilled in the art (see Huang in The~Pharmacolog"~! of Chinese Herbs, (1993), pp 69 and 287-288, CRC Press, Boca Raton, FL).:Dried rhizomes of Coptis c7Zinesis France will be verified for chemical content by quantitative chemical analysis for determination arsenic, berberine, . ;v caeruleic acid, columbamine,scopsine, coptine, coptiside-I, coptiside-II, coptisine, coreximine, epiberberine, ferulic acid, ,greemlandicine, isocoptisine, lumicaerulic acid, magnoflorine, oxybererine, thalifendine, umbellatine; urbeune, worenine, palmatine, jatrorrhizine and .. . , ..
colubamine (see also Zhu M.~ Chung Yao Tung Pao (1984) 9: 63-64). Content of the alkaloid berberine of different herbs ~sliould be between 7-9% (by weight). These data will be stored, preferably in the memory,of a computer processor, for further manipulation.
Expression biomarkersrfor standard HL include the following: NfxB; bcl-2 analog, Al;
zinc forger protein, A20; °II;-2'receptor; cell,cycle probes; c-Ki-ras2; growth regulators probes and glucocorticoid receptor,dependent.apoptosis probes (see Chi et al., Life Sci (1994) 54:
2099-2107; Yang et al:; ~NaiiW n Schrriiedebergs Arch Pharmacol (1996) 354:
102-108; Miura et al., Biochem Pharniacol:(1'997) 53; Chang K.S., J Formos Med Assoc (1991) 90: 10-14).
Alternatively; for a broader batch size, the 400,000 oligonucleotide group/1.6 cm2 chip of Affymetrix can be used ~U.S:'Pat. No.5,556,752). The expression biomarkers for standard HL
will be prepared by microairay technology as described in Example 1, including analysis and statistical data generation..Biocheinical biomarkers for standard HL include increase in glucocorticoid receptor and'inhibitiomof alpha-fetoprotein secretion in HL
exposed HepG2 cells (see Chi et al., Life-Sci;(1994) 54: 2099-2107). BBM sets are generated and analyzed as described in Example l: ~ Statistical data will then be stored, preferably in the memory of a computer processor, for'fiirther'mariipulation.
Biological responseof:'a:biosystem will be determined using cells and whole animals.
Batches of the selected herbal'coriiposition will be exposed to specific cell types, including but not limited to, human HepCi2 ~hepatoma cells, human embryonal carcinoma cells and thymocytes at concentrations°froin.0:1-100mg/ml. For animal treatments O.lmg-2g/kg of Coptic herbal composit'ion.(i.e.;'HL) will be administered orally, by intraperitoneal injection or subcutaneous inj ection. Towdetermine~ a biological response of a biosystem to standardized HL, ;., , human embryonal carciriorna~clone, NT2/D1 is exposed to various concentrations of standard HL and cells will be exariliiied for differentiation into cells with neuronal-like cell morphology (Chang K.S,, J Formos Med Assoc (1991) 90: 10-14). The assay will be repeated to generate measures and analysis will be performed as described for ginseng in Example 1.
A second independent determinatiori'of a biological response of a biosystem to standard HL will be the effect of standard HL on diarrhea due to enteroytoXigenic EscheYichia coli (ETEC). Patients with active diarrhea due to~ET.EC will~be treated with various concentrations of HL (e.g., 2g/kg) and stool volumes will be determined (see, e.g., Rabbani G.H., Dan Med Bull (1996) 43: 173-185). The assay:will be repeated to generate measures and analysis will be performed as described for ginseng imExarriple 1. The distribution sets for each biological system are n,.: ~, ,~
then put into algorithms to generate statistical values for standard HL.
Statistical data will then be stored, preferably in the memory of a computer processor, for further manipulation.
Lastly, as in Exayple~'l, the tees are reiterated to generate HBR arrays for standardized HL, wherein~the r~esultirig HBR arrays will then be used to predict biological activity and evaluate'batch qii~~ity. Using~this method, a Standardized HBR
Array can be generated~and updated periodically. ' Example 10: Evahtation of a.Selected Herbal Composition of Huang Ling Using a Subset of Variables Correlated vwith a Specific Biological Response.
To evaluate the quality of a selected test batch of an herbal composition of Huang Ling, data is first collected concerning the plant-related characteristics (e.g., plant species, plant parts, geographic origin, growth conditions, processing methods and storage conditions).
The herbal composition.is~'then manipulated such that chemical analysis can be performed to determine the chemical aopferit of the composition (see also Zhu M., Chung Yao Tun,g Pao (1984) 9: 63-64). ~ . ,, Previously obtained HL data has demonstrated terminal differentiation of human embryonal carcinoma clones into neuronal-like cells is strongly correlated with the presence of berberine (see Chang K.S:, .f.Formos Med Assoc (1991) 90: 10-14). The test batch is then exposed to test cells inclufing liuman ~embryonal, carcinoma clone, NT2/D 1 at a concentration starting at a non-toxic coiicentratiori-(determiriation of which is within the skill of the ordinary artisan). mRNA is isolated fxom exposed cells which is subsequently manipulated to serve as substrate for hybridization~~based expression monitoring of biochemical extracts using microarrays comprising~TL=2 receptor and NficB; (see Chi et al., Life ci (1994) 54: 2099-2107; Yang et al., Naunyri Sclimiedebergs Arch Pharmacol (1996) 354: 102-108;
Miura et al., Biochem Pharmacol (1997)' S3~; .Chang K.S., J Formos Med Assoc (1991) 90: 10-14; U.S. Pat.

No.5,556,752), and which~can be used to determine down regulation of c-Ki-ras2 gene expression in said.cells. ~Previou'sly obtained HL data has demonstrated terminal . , r,..
differentiation of human emliryomal carcinoma clones into neuronal-like cells is strongly I
correlated with induction~of riiitogen probes and down regulation of c-Ki-ras2 gene expression (see Chang K.S., J Formos IVIed~Assoc (1991) 90: 10-I4).
For biochemical markers; HepG2 cells are exposed to the test composition and cells are assayed for increase in glucocorticoid receptor and inhibition of alpha-fetoprotein secretion . -. a (see Chi et al., Life ci (1994) 542099-2107). Previously obtained HL data has demonstrated that inhibition of glucocorticoid induced apoptosis is strongly correlated with berberine-type alkaloids (see IVIiura et al.;Bi~chem Pharmacol (1997) 53: 1315-1322). After reiterative . ~,. . . .
analysis, data from each assay will be input into an algorithm to generate a test HBR array based on the enumerated observational data, chemical data and data concerning the subset of biomarkers.
The quality of a.test batchwill be determined by comparing test HBR and standard HL
HBR Array variables directed tovcrar~d analysis of the above observations and subsets, wherein the demonstration of tlie.induction of IL-2 receptor and NficB, the down regulation of c-Ki-ras2 gene expression, an'increase in.glucocorticoid receptor and inhibition of alpha-fetoprotein secretion for HepG2 cells (to ~inoluding data collected on growth conditions, origin, and verification of berberine alkaloid) is predictive of an equivalent BioResponse effect of the test batch on terminal differentiation of human embryonal carcinoma clones into neuronal-like cells and inhibition of dexamethasone induced apoptosis as that exhibited by standard HL.
Based on this procedure it aari be deterrriined whether or not the test batch is of a similar or different quality than that.~ofvthe:standard.
Example 11. Evaluation of Xiao Chai Hu Tang (sho-saiko-to) Using Two Bioassays. ~ , .
To evaluate the,.quali'ty'of three sources of Xiao Chai Hu Tang, two bioassays were used: 1) cell growth inhibition"and 2) hepatitis B virus secretion from infected cells. The Xiao Chai I3u Tang composition is made from a mixture of 6-7 herbal plants (Radix Bupeuri, Rhizoma Pinelliae, RhiaqinavZiyagiber°i's, Radix Scutellariae,~F~uctus Ziziphi, Codonopsis Pilosula, Radix Ginseng a~hd Radix Glycy~rhizae, see Table 2 for relative amounts, by weight).

Table B. Composition of ~iao Chef fIu Tang.
Source ~ . Plant Species Radix RhizontrtRhizontaRadix FnrctusCodonopsisRadix' Radix , .

bupleuripinelliaezingiberisscutellariae'~ziziphipilosulaginsengglycyrrhizae . * .
.

Relative Amount by Weight Singapore1 1 ; ~ ~ . ' 0.375----- 0.3750.375 ,'' ~ 0.375 ' ,'0.375 ~

,, ,._-:,.
Korea 1 0.717 ,,.. 0.492 ---- 0.429 ---- 0.288 Ø51 ' Taiwan 1 0.25 0:3750.375 0.25 ---- 0.3750.375 The three "recipe's"°~ originate iri either Singapore, Korea or Taiwan.
Batches were evaluated for toxicity aiidvfor the ability to inhibit hepatitis B virus as detected by DNA
quantitation or detection~ofhepatitis~B surface antigen (HbsAg) (see Dong et al., Proc Natl Aced Sci USA (1991) ~88:~r8495=8499).
Briefly, one grarri of preparation was added with 10 ml of water. The mixture was boiled for 30 minutes. 'The supernatant was collected after centrifugation and filtered through a 0.22 ~.m filter. Two cell,types were used: a) 2.2.15 cells which secrete hepatitis B virons (kindly provided by Professor~G.;Ace; see 'Ace et al. Proc Natl Acad Sci USA
(1987) 84: 1005-1009) and b) HepG2 cell's (ATCC~cat # HB-8065). One to fifty dilutions were used for each assay. The cell growth inhibition assay was performed for 72 hours. All other procedures were performed as described"byDong''et al., Proc Natl~Acad Sci Z~ (1991) 88:
8495-8499.
The results of the assays ,using.tfie. three batches is displayed in Table 3.
Based on these data, the Taiwan source would'lie selected as a standard herbal composition because of its low ..**, ,. . . ..-toxicity combined with its"e~fectivemess in reducing secretion HbsAG (which is proportional to viral release) by W ore 'thari-half ~ ~ ' Table 3. Bioassay of Xiao .Chaff Hu Tang (Sho-saiko-to).
Source Cell Growth.Inhibition Hepatitis (%) B Virus . (secreted) % Inhibition HepG2 , 2.2.15 DNA HbsAG

Singapore 73 100 65 38 Korea 13 ' 60 ~ 20 42 Taiwan 0 ' ~ ~ 42 0 47 The data presented in:Tables 2 and 3 for the Taiwan herbal composition constitute the initial data for the standardized HBR Array for this herbal composition.
Therefore, this data set would initially include the source of the herbal composition, the plant species and relative amounts of each the herbal, composition, and two BioResponses (i.e., cell growth inhibition and hepatitis B virus secret'ion~ froiri infected cells).
Using the procedurES setforth in the schematic of Figure 1 and in Examples l and 3, additional data can be collected on,,plant-related data, markers and BioResponses for the standard herbal compositiori.~ This .additional data is added to the initial standardized HBR
Array to generate an expanded ~stai~dardized HBR Array. Appropriate analyses of the resulting database can be conducted as set forth in the detailed description and the examples in order to ascertain the subset of variables ~hicli is most highly correlated or associated with the BioResponse of interest. 'Batch HB,R Arrays may be determined using the methods depicted in Figure 2 and in the-procedures jo'f Examples 2 and 4.
The'resultant batch HBR Array. can be compared to the standardized HBR Array so as to predict the BioResporis'~e ofvthe'batch herbal compositions.
Example 12: Herbal Preparation The standardized protocol 'for the herbal extract preparation was as follows:
The ingredients of herbal raw material's~with proper ratios were placed in a jacketed reactor and extracted with water at an eleyated'constant temperature with mixing. The solid was separated from the liquid with a_120-mesh screen. The resultant filtrate was collected and then concentrated by evaporating''the water under reduced pressure. The concentrated liquor was spray dried at elevated temperature~to yield granulated powder. This bulk substance was then formulated into the desired'ddsage form.

Example 13. Evaluation of Huang Qing Tang Huang Qing Tang (HQT)'is' an ancient Chinese botanical formula composed of four distinct herbs: Scutellariae (scute), . Glycyrrhizae (licorice), Paeo~rie lact~o~a pallus (white peo~ey root), ahd Fructus zizip7ia'(date): (Table 4). This herbal formula has been long used in Asia to treat a variety of gastrointestinal ailments since 3b0 AD.
Table 4: Herbal Ingredients of TCM Formula HQT
Scientific Common NaineTraditional Use Name Scutellariae Scute BaicalUsed to reduce capillary permeability:
Radix to reduce Skullcap inflammation: to treat enteritis xoot and dysentery: incxease the secretion of bile to treat jaundice:
to relieve muscle spasms to treat coughing: to expel parasites.

Glycyrrhizae Licorice Used to~moisten the Lungs and stop ~ Root coughs: to relax spasm Radix . . and stop pain: to moderate the action of herbs; to reduce (Gancao) ~ ~ ~ ' ~ fire and release toxins.
, Fructus ZiziphiDate ~ ~ Has diuretic and strengthening ~ effects.

Paeohie lactifloraWhite Peony Used to suppress and soothe pain;
' to soothe ligaments and pallus radix Root .'', . .purify the blood.
;'.:_ ' " , , Biolosical and EnzXrne Assays_ ; .
. , Table.5. Batch Properties (I3QT) Property Batch A ~ Batch B Batch C
~

Origin Taiwan; Sun-Ten~ Taiwan, Sun-TenTaiwan, Sun-Ten Preparation Standard Standard Boiled 30 min.
method .,,s.~ ~
~

Plant part Root ; , Root -, Eriefly, one. gram of, each :batch of Huang Qing Tang (HQT) was added with 10 ml of water (1 mg/ml).. The ~riixture,was treated~as outlined in Table 5. The supernatant was collected after centrifugation and filtered through a 0.22 ~,m filter. Two cell types were used to test for biological effects of 'each batch of HQT: a) Jurkat T cells (ATCC cat #TIB-152) and b) HepG2 cells (ATCC cat # I3B'-X065-). One to fifty dilutions were used for each assay. Frozen cells (107/m1) were quickly.thawed~in a~water bath at 37 °C. The cells were then diluted in 10 ml of pre-warmed media (see Life Technologies, Inc., Catalogue and Reference Guide, 1998-1999, Cell Culture section) followed;by centrifugation at 1500 rpm for 5 min.
The supernatant -SO-was then discarded and the cells were cultured in 100 ml media at 37 °C, S% CC~Z. After 2 days, the cells were counted (approximately 8 x 105/m1, total 100 ml).
Batches were also evaluated for the ability to inhibit hepatitis B virus as detected by DNA quantitation (see Dong et al., Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, S one gram of preparation was-'added with 10~m1 of water. The mixture was boiled for 30 minutes. The supernatant, was collected after centrifugation and filtered through a 0.22 ~,m filter. HepG2.2.1 S cells which secrete hepatitis B virons (kindly provided by Professor 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. The cell growth inhibition assay was performed for 72 hours. All other procedures were performed as described by Dong et al., Proc Natl Acad Sci USA (199I) 88: 8495-8499.
(3-glucuronidase.was assayed as HQT is known for its anti-diarrhea properties.
Different HQT extracts were added ~to triplicate wells of a 96-well plate which contained 0.lmM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed (3-glucuronidase (from E. Coli; purchased from SigmaTM) to a final volume of 80 ~,1. After 2 hr' incubation. at 37°C, the reactions~were terminated with 200 ~.1 of stopping solution which contained 0.2.M Glycine and~0.2 M NaCI (pH 10.4), and the OD was monitored with a kinetic microplate reader at S40 nm. , The results of the assays using the three batches are displayed in Table 6.
Based on these data, HQT sources A and B have relatively low toxicities combined with higher . , .
inhibitory activity relative to batch HQT C (i. e.~ approximately S fold greater toxicity toward HepG2 cells and 3.3 fold less. inhibitory activity against (3-glucuronidase than either HQT A or B, see Table 6).

Table ~6. Biological Assay of Three Preparations of HQT*
' E. Coli ~ HepG2 Jurkat HBV$
(3=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 $, % of Control represent ICso values. ' ~ . ' ND, not , determined. ~ ' , Evaluation of HOT Effects on Protein Expression . ~.y'., . ... : . ~ ~ .
HepG2 cells.(l~ x 106),~were seeded in 25 em2 flasks in 3.0 ml of RPMI-1640 medium (see Life Technologies,, Irio':,~Catalogiie and~Reference Guide, 1998-1999, Cell Culture section) 24 hr before the drug addition. . The~cells were treated with or without herbal medicine, where the former is 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 were washed twice ... .. ~.: .
with cold PBS. The cells were harvested into 1 ml of PBS and centrifuged at 10,000 rpm for 2 minutes, extracted on ice with a buffer containing 50 mM Tris-Cl (pH 7.5), 0.2 mM PMSF and 10% glycerol, followed by,three.freeze-thaw cycles. Potassium chloride was added to the cell lysate at a final concentration of 0':15' M prior to centrifugation. The protein concentration was determined and the. cell extract was electrophoresed according to the method of Laemmli (Ncztu~e (1970) 227;680-685). Western.blots were performed by standard techniques known in the art, see for example S ~ainbrook, et al (1989).. The antibodies used were directed to the following proteins:.Topo °Z; Sfat'(20Z07); Cyclin Bl; MAPK (Ab2) and Nm 23 H1.
Figure 4 demonstxat~es~tlia~ the higher concentrations of HQT A or HQT B
differentially effects the expression of c~cliri Bl,polypeptide.
HPLC Analysis :. . ,.._, ,. , . , . , . , ..- . . ,.. , . .~ ~ ..,_.
The herbal batches~v~ere analyzed by HPLC with a Beckman ODS UltrasphereTM
column (5 micron particles, 4.6 mm X 25 cm) and detected with an UV
spectrophotometer (Perkin Eliner). The vvavelerigths for UV' detection were monitored at 280 nm and 340 run. The mobile phase was pumped~at ~1'rril/min'and consisted of Solvent A: H20 and Solvent B: 20%

MeOH with the following gradient: 1) the solvent was 100% solvent A for the first S minutes;
2) the sblvent composition vvas changed to 10% solvent A / 90% solvent B for the next 10 minutes; and 3) the solvent was changed to 10% solvent A / 90% solvent B for the next 40 minutes. This was followed~liy..the'addition of 100 % solvent A for S minutes.
The HPLC
markers are baicalin aridy~li'aicaleiii. '.
Mass Spectrometry The.herbal extract'vvas analyzed by MarinerTM ESI-TOF Mass Spectrometry (MS) from PE Biosystems. Control tracings were generated using baicalein and baicalin, two known active ingredients in HQT: ' ' ' ' ' ' HQT samples in water aild~acid treated batches were been analyzed by HPLC and Mass Spectrometry. While.watef treated HQT batches A and B had distinct HPLC and MS
tracings, acid treated batches gave alinbst identical patterns (data not shown).
Algorithm y - ~ . ' 't : : . , .
The data collect'ed~°forin part of the multidimensional analysis used to generate 1S multivariant normal distribiitiom sets as a~means of determining a baseline correlation between biological activity and standard HQT chemical (HPLC and Mass Spec), arid origin/growth characteristics: ~ . ~~ ' Example 14. Indiw7ic~ual components A. Licorice. ' ~ ; ~ , Evaluation of Glvcvr~hizae Radix ~corice~
Licorice is useful for moistening the lungs and reducing coughs, helps to relax spasm ~.Y '.. , ' and pain. The properties of the licorice batches used in this example are presented in Table 7.
..~;, ~:f~_'.',;
Talole'7. Batch Properties (licorice) Property . .~atch~.A:; .Batch Batch C Batch D
, :y~ B

Plant NameGlycyrrhizaev.'~'Glycyri~hizaeGlycyrrhizaeGlycyrrhizae :': ' . Radix : . Radix Radix Radix ' , .

Origin Imier MongoliaInner MongoliaU.S., Kin U.S., Kin Man Man ' ~ Herb Center Herb Center PrepaxationStandard Standard Boiled 30 Warm H20, , - min. 30 method min.

Plant partRoot , ' Root _ - -~

ZS

Biological and Enz~!me Assa,~, To assay the quality, of herbal sources, each herbal extract supernatant was assayed and the analysis was repeated three: times. For a given sample to be assayed, 1 gram of herbal powder was dissolved in 10 ml of ~0° C deionized water (neutral pH) in a polypropylene tube.
The tube was then incubated as outlined in Table 7, then centrifuged to obtain the supernatant.
Batches of licorice were tested against either HepG2 cells (ATCC cat # HB-8065) or Jurkat T
cells (ATCC cat #TIB-152) or.both. Cells.were cultured for 24 hours as described above.
Batches were also evaluated for the ability to inhibit hepatitis B virus as detected by DNA quantitation (see Dong et al.,,Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, one gram of preparation.was added with 10 ml of water. The mixture was boiled for 30 minutes. The supernatant was collected after centrifugation and filtered through a 0.22 p,m filter.2.2.15 cells which secrete hepatitis B virons (kindly provided by Professor 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.,~ .The~cell growth inhibition assay was performed for 72 hours. All other procedures v'vere~performed as described by Dong et al., Proc Natl Acad Sci SSA (1991) 88: 8495=8499:' °"
Again, (3-glucuronidase was assayed. Different'licorice extracts were added to triplicate wells of a 96-well plate which contained O.lmM phenolphthalein glucuronidate, 70 mM Tris-. . .
HCl (pH 6.8) and 0.8 ng of dialyzed beta-glucuronidase (from E.Coli, purchased from Sigma) to a final volume~of 80 ~,l and'assayed as above.
The results of the assays using the two batches is displayed in Table 8. Based on these data, licorice batch A was much more toxic to Jurkat cells than batches B
(approximately 9 fold) and a more effective inhibitor of (3-glucuronidase (see Table 8).
'Table 8. Biologica~.Assay of Four Preparations of Licorice*
y . E: Coli'. , IiepG2 Jurkat HBV$

(3-Glucuronidase DNA

Licorice A . -' 1.1 ' ~ ~ , 1.07 0.41 None Licorice B ~ , : ND '' ND 3:6 ND

Licorice C , . , r 2.1 . ND ND ND
Licorice D . .:ND "~ ND - >2.0 53.8 *Values represent ICSO , $, % of Control values. r, ND, not determined.

Expression Assay In order to assay gene expression, Jurkat T cells were treated with herbal extract as follows: Jurkat cells (107/m1) °were quickly thawed in a water bath at 37 °C. The cells were .. , .~..
then diluted in 10 ml of pre-warnied media (see Life Technologies, Inc., Catalogue and Reference Guide, 1998-1999; Cell Culture section) followed by centrifugation at 1500 rpm for 5 min. The supernatant was.then'discarded and the cells were cultured in 100 ml media at 37 °C, 5% CO2. After 2 days~,'the cells. were counted (approximately 8 x 105/m1, total 100 ml).
The herbal extract solution was prepared as outlined above (e.g., 2 g of an herbal powder to obtain 20 nil of sterile 'solution (0.1 g/ml). The cells were divided into 3 flasks at a density of 2.5 x 105/m1, 1~OO m1/eacli flask. Assays were carried out with control (no extract), and 10 ml of extract at I O mg/ml,_ and 1 mg/ml. Again, toxicity results were used to determine the "high" and "low"' concentrations for any given extract. After extract addition, cell cultures were incubated for X24 hours' under conditions as outlined above. The cells were counted and subsequently collected im Sa~uril centrifuge tubes. The resulting cell pellet was treated with an RNA isolation means to extract~ri~RNA (see, for example, Sambrook et al., 1989 at pages 7.3-7.39). . , Microarrav Microarray printirigFwas carried out as follows:
Human gene olones'yvere obtained from the IMAGE Consortium libraries through its distributors and comprise'geries from various tissues. Most clones have been partially sequenced and the sequericesv vveie-available as expressed sequence tags in the dbEST database of GenEank. Clones were cultured and,amplified using commercially available primers prior to application on nylon meriibraries ~(Chen et al., Genomics (1998) 51:313-324).
Approximately 10 ng of each amplified target~:was applied on a positively charged nylon membrane using a PG
(personal computer) controlledvarraying system. The arraying system allows high density spotting and is capable of depositing 31,000 spots on a piece of nylon membrane measuring 18 by 27 mm using a 24-pin arraying tool.
cI~NA probe and Membrane Hybridization Two microgram ofyeacli rnRNA sample (mRNA was isolated as outlined above) was _ ., s labeled with biotin and~or~ digoXigeiiin using random primed reverse transcription. The labeled samples were treated'with alkali and the resulting labeled nucleic acids were precipitated prior to use in hybridization: Membrane ~liybridization and washing were carried out using the labeled probes as disclosed iri~.Clien~et al. (1998). To detect the spots on the membrane in dual color mode (i. e., both biotin and digoxigenin), j3-galactosidase-conjugated streptavidin (Strept-Gal) and alkaline phosphafase-conjugated ~digoxigenin antibody (anti-Dig-AP) were employed.
After color development, image digitization using an imaging means was employed (e.g., a flatbed scanner or digital .camera). Quantitative measurements were determined by computer . :~.~~., ..
analysis which uses a program that measures the integrated density of the primary color ...v . ;',.
components of each spot, performs regression analysis of the integrated density data anal locates statistical outliers asdifferentially expressed genes.
Gene expression data for samples 1, 2 and licorice fST117) Extract 1, 2 and 6 corresponding to extract of Cordyceps sinensis, Po~ia cocos (ST
027) and licorice, respectively, ,were assayed by the following method:
Batches were evaluated for toxicity using Jurkat T°c.ells..
The eXtracts were' prepared as outlined in Example 6. The cells were divided into 24 well culture plates by addiiig~'l:,~ml~of Jurkat~cells at a density of 5 x 1OS/ml. Assays were carried out with control (no~ ~Xtract)and 5 concentrations of extracts as described (see Table 9). The high and low concentrations for the cell culture assays were varied between 10 mg/ml and 0.05 mg/ml (i. e., mg dry ~?veight of herbal extract per ml) depending on the toxicity of the extract to cells. For certain sariiples the toxicities at 10 mg/ml were such that "high" and °'low"
concentrations were adjusted ~downv~ard, nevertheless, at least one order of magnitude between extremes was maintained.v'For'exaxnple; for licorice (ST117) the "high" was 0.5 mg/ml and the "low" was 0.05 mg/ml (see,Table.:9). After extract addition, cell cultures were incubated for 24 hours under conditions vas. outlined in Example 6. The cells were counted and the resulting data tabulated to demonstrate extract toxicity. The resulting data is shown in Table 9.

Table 9.
Survival Cell Number at Different Concentration of Herbal Extract Solution . experiment concentration no.x105m1 . S 2 1 O.S mg/ml High conc Low 10 mg/mlmg/ml'mglmlNo drug conc.
mg/ml (mg/ml) (mg/ml) .

1 Cordyceps 8.4 11.9 11.5 9.2 9.0 12.4 10 1 sinensis ', mycelium ~

2 ST027 4.2 ~ 10 7.5 10 10 10 1 8.7 , 3 STO-44 ' x':5.98.4 9.9 9.4 S 0.5 -4 STOSl - - 1.7 S.4 0.5 O.OS

S ST093 - - .1.9 3.8 4.4 O.S O.OS
.

,.
6 STI17 . . ~ 4.6 S.8 O.S O.OS
1'.6.~3.6 7 ST123 3.4 ' 8 9.3 7.8 5 O.S
6.4' ~

8 ST128 3.5 .' ~ 7.7 8.3 S O.S
v .7.7.7.9 9 STI34 2.9' ' 11.2 9.6 9.8 5 O.S
~ ~6.1,..,.
"

ST237 - . 2.5 6.6 8.7 1 0.1 Note: original cell number is SX10'/ml and the number to 1OX10'/ml after 24h incubation. "-°' describes all dead cells:
5 Protocol: : ,.. ' .
1. Add 1 ml of Sx105/inl Jurkat cells into 24 well culture plates.
2. Prepare 12 kinds of herbal~,extract solutions and sterilize.
3. Test 5 concentration per s'ample~ 1,0 mg/ml, 5 mg/ml, 2 mg/ml, 1 mg/ml, 0.5 mg/ml 4. Culture the cells for 24.'hvirl 37C with 5% C02 incubator.
10 ' '. . ' In each analysis; 144X96 genes (i.e., 13,824 genes) were analyzed (data not shown) and about 100 genes showed ~sigriificamt differences in comparison with that of control (Table 10). Some of the,genes were'up-regulated and others were down-regulated. The magnitude of the difference with the control. sometirn.es varied depending on the relative amount of the herbal composition to.which~ the particular cells' were exposed. Numbers under C1 (control treatment) and H or L (herbs) represent intensities of mRNA expressed after subtraction of background (Table 10). The gene designation is encoded in Array AD, which can be traced to a specific GenBanlc clone.' The level of expression was determined by H or L
divided by C.
Only a fraction of 13,824 genes iri each herb treated samples showed significant changes, namely, up, down or unchanged (see Table 10).

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SUBSTITUTE SHEET (RULE 26) In this manner, we are able to correlate specific gene expression with the exposure of a cell to no, low (L) or high (H) amounts of an herbal composition. Many of the genes identified in this way code for proteins important in known metabolic or biochemical pathways. Many of these proteins have direct and indirect effects on certain physiological, morphological and psychological parameters: Thus, this method permits the association of a particular genetic fingerprint of an herbal composition with its array biological effects. Such associations can be used to profile or characterize an herbal composition for the purposes of Quality Control and Quality Assurance and evaluating pharmacological or toxicological properties.
The role of primary and secondary herbs in an herbal formula can also be assessed by this approach.
HPLC Anal.~is The herbal batches were analyzed by HPLC with a Beckman ODS LTltrasphereTM
column (5 micron particles, 4.6 mm X' 25 cm) and detected with an W
spectrophotometer (I'erl~in Elmer). The 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: HZO and Solvent B: 20%
MeOH with the following gradient: 1) the solvent was 100% solvent A for the first 5 minutes;
2) the solvent composition Was changed to 10% solvent A l 90% solvent B for the next 10 minutes; and 3) the solvent'was changed to 10% solvent A / 90% solvent B for the next 40 minutes. This was followed by the addition of 100 % solvent A for 5 minutes.
The HPLC
marker is glycyrrhizin. ' A1 og rithm The data collected form part of the multidimensional analysis used to generate multivariant normal distribution sets as a means of determining a baseline correlation between biological activity and standard licorice molecular, chemical (HPLC and Mass Spec), and origin/growth characteristics.
B. Scute Evaluation of Radix~Scutellaf~iae (Scutel Scute has been found to be useful in reducing capillary permeability and inflammation.
It can also be used treat eri~eritis''and dysentery, increases the secretion of bile to treat j aundice;
to relieve muscle spasms; to~ treat coughizig and to expel parasites. The properties of the scute batches used in this example are presented in Table 11.

-Table 11. Batch Properties (Scute) Property Batch A Batch B Batch C Batch D
.

Plant NameScutellariaeScutellariaeScutellariaeScutellariae ~ radix radix radix radix Origin Sanxi Province.U.S., Kin U.S., Kin U.S., Boston Man Herb Man Center Herb Center PreparationStandard Boiled, 30 Warm H20, Soiled , 2 method : , min 30 hours . . min.

Plant partRoot - - -Biological and Enz~tme Assavs Briefly, one gram of each preparation of scute extract was added with 10 ml of water (1 rng/ml). The mixture was treated as Outlined in Table 11. The supernatant was collected after centrifugation and filtered through a 0:22 p,m filter. Batches of scute were tested against either HepG2 cells (ATCC cat #~HB-8065) or Jurkat T cells (ATCC cat #TIB-152) or both. One to fifty dilutions were used for each assay. Cells were cultured for 24 hours as described above.
Batches were also evaluated for the ability to inhibit hepatitis B virus as detected by DNA quantitation (see Dong et al.; Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, one gram of preparation was added with 10 ml of water. The mixture was treated as outlined in Table 11. The supernatant was collected after centrifugation and filtered through a 0.22 ~m filter.2.2.15 cells which secrete hepatitis B virons (kindly provided by Professor G. Ace; see Ace et al. Proc Natl Acad S'ci~USA'~~(1987) 84: 1005-1009) were used in this assay. One to fifty dilutions were used for each assay_ ' The cell growth inhibition assay was performed for 72 hours. All other procedures were performed as' described by Dong et al., Proc Natl Acad Sci LTSA (1991) 88: 8495-8499:
For (3-glucuronidase, different scute extracts were added to triplicate wells of a 96-well plate which contained O.lmM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH
6.8) and 0.8 ng of dialyzed [3-glucuroriidase (from E. Coli, purchased from Sigma) to a final volume of 80 ~tl. After 2 hr incubation.at 37°C,~the reactions were terminated with 200 ~.1 of stopping solution which contained 0.2 M Glycine and 0.2 M NaCI (pH 10.4), and the OD
was monitored with a kinetic microplate reader at 540 nm.

The results of the assays using the three batches is displayed in Table 12.

Table 12. Biological Assay of Four arations Prep of Scute*

. . "'E. Coli HepG2 Jurkat HBV$ DNA

(3-Glucuronidase Scute A ~..,1.5,. 0.33 0.45 None Scute B ~ ' ; ' l., g ~,. ; ,' , ND ND
. ND

Scute C ' ~ ' . - "Ø3 ' ~ ND ND ND

Scute D ND " 0.65 ND 27.5 *Values represent $, % of Control ICS° values.
ND, not determined. ' e.
Evaluation of Scute Effects orl~Protein Expression HepG2 cells (1 x' 10~) were seeded in 25 cm2 flasks in 3.0 ml of RPMI-1640 medium (see Life Technologies, Inc.,~Gatalogue and Reference Guide, 1998-1999, Cell Culture section) 24 hr before the extract' addition. The cells were treated with or without herbal medicine, where the former is 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 were washed twice with cold PBS. The cells were harvested into 1 ml of PBS and centrifuged at 10,000 rpm for 2 minutes, extracted on ice with a buffer containing 50 mM Tris-Cl (pH
7.5), 0.2 mM
PMSF and 10% glycerol, 'followed by three freeze-thaw cycles. Potassium chloride was added to the cell lysate at a final concentration of 0.15 M prior to centrifugation.
The protein concentration was detertTiirieei'arid,the cell extract was electrophoresed according to the method of Laemmli U.K. (Nature (1970 227:680-685): Western blots were performed by standard techniques knowr'i in the art, see for example Sambrook, et al (1989). The antibodies used were directed to the folloWing'proteins: Topo I; Stat (20707); Cyclin B1; MAPK
(l-~b2) and Nm 23 H1.
Figure 4 demonstrates that scute batches A and B do not differentially affect the expression of the polypeptides~resolved on Western blots.
HPLC Anal, ' . . . .
The herbal batches wefeyanalyzed by'HPLC with a Beckman ODS UltrasphereT'~ , column (5 micron particles, ~.~6' ri~xri X 25 cm) and detected with an UV
spectrophotometer (Perkin Elmer). The wavelengths for U~ detection were monitored at 280 nm and 340 nm. The mobile phase~was pumped~at'1 iril/rriin and consisted of Solvent A: HaO and Solvent B: 20%

lVIeOH with the following grac~iemt: 1) the solvent was 100% solvent A for the first 5 minutes;
2) the solvent composition was changed to 10% solvent A / 90% solvent B for the next 10 minutes; and 3) the solvent was changed to 10% solvent A / 90% solvent B for the next 40 minutes. This was followed by the addition of 100 % solvent A for 5 minutes.
The HPLC
markers are baicalin and baicalein. .
Scute batches in water and acid treated samples were analyzed by HPLC. Water and acid treated batches were virtually indistinguishable.
Al o The data collected form part of the multidimensional analysis used to generate multivariant normal distribution sets as a means of determining a baseline correlation between biological activity and~standard scute chemical (HPLC), and origin/growth characteristics.
C. White Peon, Root'''' ' I ~ .
Evaluation of Paeorcie lacti of°a callus radix (Peony) Peony is used to suppress ~amd soothe pain. It is also known to soothe ligaments and purify the blood. The properties of the peony batches used in this example are presented in Table 13.
,' ~ , ~,able~ l3. Batch Properties (Peony) Property ' ~ 'Batch A Batch B

Plant Name Paeoiiie lact~o~aPaeonie lact~ora pallus pallus Origin Anwey,~'rovince iJ.S., Boston "

Preparation Standard'" Boiled 2 hours.
method :~';

Plant part . Root ' ' . . Root Biological and Enzyme Assays Briefly, one gram of each preparation of scute extract was added with 10 ml of water (1 mg/ml). The mixture was treated .as outlined. in Table 13. The supernatant was collected after centrifugation and filteredvthrough a 0.22 ~,m filter. Batches of peony were tested against either HepG2 cells (ATCC cat # HB-8065) or Jurkat T cells (ATCC cat #TIB-152) or both. One to fifty dilutions were used fore each assay.' Cells 'were cultured for 24 hours as described above.
Batches were also .evaluated for the ability to inhibit hepatitis B virus as detected by I~NA quantitation (see Dong.~t~al:,°,Proc Natl Acad Sci USA (1991) 88:
8495-8499).

..
Briefly, one gram of preparation was added with 10 ml of water. The mixture was treated as outlined in Table 13. ~~The supernatant was collected after centrifugation and filtered through a 0.22 ~.m filter: , 2.2,:15 cells~which secrete hepatitis B virons (kindly provided by Professor G. Ace; see Ace,et al. Proc Natl Acad Sci USA (1987) 84: 1005-1009) were used in this assay. One to fifty dilutioris were used for each assay. The cell growth inhibition assay was performed fox 72 hours~:~ All~other procedures were performed as described by Dong et czl., Proc Natl Acad Sci USA (1991) 88: 8495-8499.
Different peony extracts were added to triplicate wells of a 96-well plate which contained O.lmM phenolphthalein glucuronidate, 70 mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed beta-glucuronidase (from E. eoli, purchased from Sigma) to a final volume of 80 p,1.
After 2 hr incubation at 37°C.,-the reactions were terminated with 200 ~.l of stopping solution which contained 0.21VI Glycine :aiid 0.2 M NaCI (pH 10.4), and the OD was monitored with. a kinetic microplate reader at: 540 nm. Results are shown in Table 14.
Table 14. Biological Assay of Two Preparations of Peony*
''; : E. Cola , IIepG2 Jurkat I3B~l~

(3=Glucuronidase Peony A . 2.8 ~ >1.5 1.1 None Peony B >2.5 ' ND ND ND

*values represent$, % of Control ICS

values. . ' ND, not determined., HPLC Analysis -The herbal batches were analyzed by HPL,C with a Beckman ODS Ultrasphere column (5 micron particles, 4.6°mm X 25 cin) and detected with an UiT
spectrophotometer (Perkin lElmer). The wavelengths for UV detection were monitored at 280 nm and 340 nm.
The mobile phase was pumped at 1 ml/rinri arid~consisted~ of Solvent A: Ha0 and Solvent B: 20% MeOH
with the following gradient::l).thevsolvent was 100% solvent A for the first 5 minutes; 2) the .. . . . :. ..,. .
solvent composition was changed to 10% solvent A / 90% solvent B for the next 10 minutes;
and 3) the solvent was changed to 10% solvent A / 90% solvent B for the next 40 minutes.
This was followed by the addition~of ~l~0 % solvent A for 5 minutes. HPLC
marker is paeoniflorin.
Peony batches were analyzed~by HPLC as shown in Figure 5.
A1 o_g rithm_ The data collected foim part-of the multidimensional analysis used to generate multivariant normal distribution sets;as a means of determining a baseline correlation between biological activity and standard peony chemical (HPI,C), and origin/growth characteristics.
D. Date Evaluation of Zizi~hi F~uctics (Date) Date has been used fondiuretic properties and strengthening effects. The properties of the date batches used in this example~are presented in Table 15.
,. . .
Table 15. Batch Properties (Date) . . . ,.. .
Property ~ Satch 'A~~. ~ Batch B Batch C
~

Plant Name Ziziphi F,ructus.~Ziaiphi FructusZiziphi Fructus Origin Hebe'i Province.U.S., Kin Man U.S., Kin Man Herb Herb Center Center Preparation Standard',. Boiled, 30 Warm HZO, 30 method . , ~ min min.

Plant part Fruit . ~ - -, ~

Biological and Enz~nne Assays Briefly, one gram of each batch of scute extract was added with 10 ml of water (1 mg/rill). The mixture waswtreated as~:outlined in Table 15. The supernatant was collected after centrifugation and filtered through a 0.22 ~,m filter. Batches of date were tested against either HepG2 cells (ATCC cat#;HB-8065) or~Jurkat T cells (ATCC cat #TIB-152) or both.
One to fifty dilutions were used,for-each, assay. Cells were cultured for 24 hours as described above.
Batches were also evaluated for the ability to inhibit hepatitis B virus as detected by DNA quantitation (see Doug et~al., Proc Natl Acad Sci USA (1991) 88: 8495-8499). Briefly, one gram of preparation was added with.l0 ml of water. The mixture was treated as outlined in Table 15. The supernatant was collected after centrifugation and filtered through a 0.22 ~,m filter. HepG2.2.15 cells which secrete hepatitis B virons (kindly provided by Professor G.
Ace; see Ace et al. Proc N~,tl Acad Sci USA (1987) 84: 1005-1009) were used in this assay.
One to fifty dilutions were used for each assay. The cell growth inhibition assay was performed for 72 hours. ~ All other procedures were performed as described by Dong et al., .
Proc Natl Acad Sci USA (1991) 888495-8499.
Different peony eXtzacts were added to triplicate wells of a 96-well plate which contained O.lmM phenolphthalein.glucuronidate, 70. mM Tris-HCl (pH 6.8) and 0.8 ng of dialyzed beta-glucuronidase (from E. Coli, purchased from Sigma) to a final volume of 80 ~.1.
After 2 hr incubatiomat~37°C, the. reactions were terminated with 200 ~,1 of stopping solution which contained 0.2 M Glycirie arid 0.2 M NaCI (pH 10.4), and the OD was monitored with a kinetic microplate reader at 540 iim. Results are shown in Table 16.
Table 16. Biological Assay of Three Preparations of Date*
E: Coli HepG2 Jurkat HBV$ DNA

(3-Glucuronidase Date A ~1~:2 ~ 1.5 v 5.1 None . . . ', Date B ~ ND >2.0 ND 52.3 Date C r 2.5 ~ ND ND ND

*Values represent$, % of Comtrol .

ICS values. . ' , ND, not determined.;, , I0 HPLC Anal, sis The herbal batches.were analyzed by HPLC with a Beckman ODS Ultrasphere column (5 micron particles, 4.6 mm X.25 cm) and detected with an UV spectrophotometer (Perkin Elmer). The wavelengths fb~ UV detection were monitored at 280 nm and 340 nm.
The mobile phase was pumped at l~ml/min.and consist of Solvent A: H20 and Solvent B: 20%
MeOH with the following gradient: 1) the solvent was 100% solvent A for the first 5 minutes; 2) the solvent composition was changed to f0% solvent A / 90% solvent B for the next 10 minutes;
and 3) the solvent was changed to 10% solvent A / 90% solvent B for the next 40 minutes.
This was followed by the addition of.100 %~ solvent A for 5 minutes. HPLC
markers for date are chelidonic acid and cAlVIl':~ ' Date batches samples were ~.nalyzed by HPLC as shown in Figure 6.
Al og n The data collected form part of the multidimensional analysis used to generate multivariant normal distribution sets as a means of determining a baseline correlation between biological activity and standard peony chemical ~(HI'LC), and origin/growth characteristics.

Example 15. Characterizing Herbal Medicines b~~lVucleic Acid Microarra~
Anal~ s~-is. .
Introduction.
The rapid development of nucleic acid microarray technology has led to an explosion of gene expression data, (Larder, 1999, Duggan et al., 1999). Four characteristics of the gene expression account for the great value of using nucleic acid microarrays to study the gene expression profiles. (i) Nucleic acid microarray makes it easier to measure the transcripts of thousands of genes at once. (ii) Close association between the function of a gene product and its expression pattern makes gene function predictable. (iii) Cells respond to the micro-environmental changes by changing the expression level of specific genes. (iv) The sets of genes expressed in a cell. determine,what the cell is derived of, what biochemical and regulatory systemsare involved; and so on (Brown and Botstein, 1999). By using a microarray system, the above features can be studied in an ensemble manner.
The expression of any. desired number of genes can be detected using the nucleic acid microarray technology: 'For' example, up to about 20;000 genes may be placed on a single array. i~Ve have developed a nucleic acid microarray with colorimetric detection system (Microarray/CD) (Chen ~et al., 1998). Gene expression profiles of different cell lines were studied using microarray filter'rnembranes (2.7 cm x 1..8 cm) with about 10,000 cDNA
representing approximately 10;000: distinct human transcripts. The sensitivity and detection limits of the microarray/CD 'system have been characterized and are comparable to the system with radioactive detectiori'~r'tfie system with laser induced fluorescence detection (Bertucci et al.; 1999). ~f; ;.; ~, , . , , As previously descriliec~', cellular gene expression profiles portray the origin, the present differentiation of ~tlie cell; and the cellular responses to external stimulants. In other words, the gene expressiori~.profiles reveal the, state of the cell and microarray is a perfect tool for the rendering purpbse. 'In'tlie present 'studies, we apply the microarray/CD system to characterize cellular responses ~to external stimulants, in this case, the Chinese herbal medicines. Conversely, we also based on the stimulated gene expression profiles to classify different herbal mediciries:e' Figure 7 is a flowchart depicting a general method that may be used for establishing an expression response data~set for~cells treated with an herbal composition. The method comprises the steps of (a) Determine the' ICSO concentration of~an herbal composition by incubating various concentrations of the herbal medicine in mammalian cell cultures and identify the concentration that leaves 50% of survival cells after a predetermined time.
(b) Incubate the mammalian cell 'cultures with herbal extracts of various fractions of ;. .
ICSO concentrations:
(c) Harvest and count the~cultured cells after a predetermined culture time.
(d) Immediately lyse the cells after they are removed from the incubator and extract mRNA from cell lysate.
(e) Label the mRNA byreverse transcription reaction to turn mRNA into labeled cDNA.
(f) Mix the labeled cIDNA with control cDNA of plant origin and perform hybridization to a microarray of mammalian gene probes.
(g) Measure expression ;level of genes by 'analyzing digitized images of the microarray hybridization results:
(h) Perform data pre=processing' to select data for statistical analysis.
(r) Acquired'expression data generated by microarray experiments of an herbal composition with various concentrations.
(j) Data pre-processing to select the genes with statistical significance in cells treated with different concentrations of the herbal medicine.
(k) Categorize expression profiles into clusters by statistical methods such as the self organizing-map algorithm:
(1) Deduce the characteristic expression profiles for the herbal medicine based on the expression profile clusters.
Figure 8 is a flowchart demonstrating how data sets of expression data for various batches of the herbal compositiow are integrated to make an expression profile database for the particular herbal composition. The expression profile database then becomes part of the HBR Array.
HBR,Arrays contaiiiirig expression profiles may also be used to identify an unknown herbal composition. Figure 9 is a'flowchart depicting a general method for identifying an unknown herbal composition, the method comprising the steps of (a) Construct an HBR Array containing characteristic expression profiles for an herbal medicine or a collection of expression profiles. of various herbal medicines by the ..
aforementioned steps. ' (b) Obtain the characteristic expression prof 1e data set of the unknown herbal composition.

(c) Compare the HBR Array~~coiitaining the characteristic expression profile induced by the said unknov~m herbal composition with a standardized HBR Array containing expression data by~algorithms such as the Hamming distance algorithm.
(d) Score possible alignments to identify the most probable herbal composition whose characteristic expression profiles are archived in the said HBR Array Scoring possible alignments of HBR Arrays containing expression profiles may be performed using hierarchical cluster analysis of the Hamming distance matrix. ~Jse of hierarchical cluster analysis for the Hamming distance matrix is well known in the art.
The gene expression profiles may also be incorporated into the standardized HBR Array.
As has been already discussed, the standardized HBR Array containing such gene expression profiles induced by an herbal composition can be used for studying the pharmacological ~.
mechanisms of the herbal; composition, for discovering new application of the herbal composition, and for designing optimized formulation of a complex herbal preparation. As can be seen from the flowchart.of Figure I0, 'the method may be generally outlined as comprising the steps of (a) Construct a data set coritairiing the characteristic gene expression profiles for an herbal composition:' (b) Score each gene li"'y the consistency of its expression profiles in the data set using known statistical liarameters, such as the coefficient of variation.
(c) Based on the statistical scoring, gene expression profiles for an herbal medicine are selected to be incorporated into the standardized HBR Array.
HBR Arrays containing-gene~expression profiles may also be used to identify signature gene expression profiles Yinduced; b~y individual chemical constituents in an herbal composition consisting of complex chemical 'constituents, as outlined in the flowchart of Figure 11. The method comprises the steps. of (a) Construct a HBO Array. containing characteristic gene expression profiles for an herbal compositidn~by'~the aforementioned steps.
(b) Determine the cornpositiori of chemical constituents in an herbal medicine by high performance liquid cl~om~.tography (HPLC) or liquid chromatography mass spectrometrsr (LC-MASS).' (c) Repeat the step (1i) for'varibits batches of herbal medicine preparations.
(d) Score the correlat'ioti'coefficients between the expression levels of each gene with the amount of individual chemical constituent in an herbal preparation.

(e) The signature gene expression profiles for individual chemical constituent are selected with a Pearson correlation coefficient exceeding 0.99 or smaller 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, one can choose any number of genes that "are differentially expressed to be included in the data set represented the gene expression profiles. !For example; one may choose about 10 genes, about 100 genes, r ".
about 500 genes, about"1000 genes, about 1500 genes, abut 2000 genes, about 2500 genes or more, or any number in between.
The prescription of the" Chinese herbal medicine Scute and Licorice combination (Huang Chin Tang) stops diarrhea, relieves spasms and clears fever. The ingredients of Huang Chin Tang are Scute, .Peony, "Licorice and Jujube. This recipe has been used for more than 1000 years but the clieriiical and biomedical studies oy the prescription have not been carried out until recent decades.;Iri~this study we used the nucleic acid microarray technology to study the gene expression profiles'~of'.h'erba'1 medicines treated cells. Our aims are to demonstrate the feasibility of using the rriicro'array/CD system for classification of different herbal compositions or different'prepaxations and to find the predictor genes (marker genes) for the Huang Chin~Tang prescription. The lomg-term goals are to find the correlation of the biochemical 'ingredierits'in ~eacli herbal composition with the gene expression profiles of various treated cells arid to decipher the molecular pharmacological mechanisms of the Chinese herbal medicines ima~rational fashion.
Materials and Methodse ~ ~ " ~ ' 1. I)e~ei~pnnent of;'a'vcell banking systegn. , Purpose: ' Microarray,~ystem is a sensitive detection method to monitor gene expression patterns of:cells"It' is necessary "to build a Cell Banking System with a Master Cell Bank (MCB) '.and a Working Cell Bank (WCB) to minimize cell variability for herbal medicine testing. .
Scope: The Cell Baxik System is used for all types of cells in microarray studies.
Apparatus: " C02 Air-Jacketed~Incubator (NUAIRETM DH autoflow) Centrifuge (KI~BOTA 2100) Freezingwial~:(Coi'ning Costar, Cat. #430659) Tissue culture flask 750 ml (Falcon, Cat. #3045) Tissue culture dish "150x25 mm (Falcon, Cat. #3025) Cell: Jurkat T cell~~from~Dr: Alexandra Ho Reagents: RPMI Medium 1640 (GIBCO BRL, Cat. #31800-014) Dimethyl Sulphoxide (DMSO) (Sigma, Cat. #D-2650) Fetal Bovine Serum (HyClone, Cat. #5H30070.03, Lot#AGL7258) 2-mercaptoethanol (GIBCO BRL, Cat. #21985-023, SxlO-Z M) Media: I. Culture medium: 90% RPMI + 10% fetal bovine serum + 2-mercaptoethanol (5x10-5 M) II. Freezing medium: 90% RPMI 1640 + 10% DMS~
Procedure:
A. Master Cell Bank ..
1. Follow the standard sterile procedure of cell culture.
2. Seed Jurkat T cells'in culture medium in flask at 37°C with 5% COZ
incubator.
3. After incubation for two days, count the cell number and spread the cells to two flasks.
Note. Cell density 1s kept a°~~out 5x104- 2x10 per ml.
4. Culture and count the cell riuinber until~the number reaches 2x107.
5. Collect the cells iri'S0'ml centrifuge tubes and spin at 1300 rpm (300~g) for 5 min.
6. ' Discard the supernatant and re-suspend the cell pellet with chilled freezing media. The cell number in eachJvial is about fx106 per ml.
7. Slowly freeze the cells:by the following temperature profile: -20°C
for 2 hr, -80°C for 24 hr and then place the cells in liquid N2 storage. Store a total of 20 frozen vials in MCB. , . ', B. T~Yorkihg Cell Bank. ' . ' t .
1. Retrieve one vial of ~ell~ from MCB in liquid N2 tank and quickly thaw at 37°C water bath. ' ; y ' 2. Transfer the cells into ,1,0 ml of warm culture medium.
3. Spin down the cells at 1300 rpm ~(300Xg) for 5 min. Discard the supernatant. Culture the cells with 20 ml 'medium in a flask. ' 4. Sub-culture the cells to~ 2 flasks. ~ .
5. Seed 5x107 cells with 500 ml culture medium in each flask with stirring for a total of 2 flasks. Culture the'cells~for 2 days.
6. Culture until the cell density reaches 1x1061rn1 arid a total volume of 1 L.
7. Prepare freezing media by adding 100 ml of fetal bovine serum and 10 ml of DMSO.

.72_ 8. Centrifuge, discard the supernatant and re-suspend the cells to 110 ml of freezing medium. . . ' 9. Dispense 1 'rnl to every freezing vial (10 million cells per vial) for a total of 100 vials.
Slowly freeze the cells by;the temperature profile described above.
2. Determination of growth inhibition concentration of herbal extract in cell cultures.

Purpose: Most drugs are toxic to cells. This experiment is designed to examine the toxicity of herbal extracts in Jurkat T cells and to determine the growth inhibition concentration of herbal extracts that keeps the cells alive.

Scope: This assay'can be used in all kinds of herbal extracts to examine the toxicity.

Apparatus: COz Air-Jacketed,Incubator (NUAIRETM DH autoflow) Counting chamber (Hemacytometer, Reichert, USA) Microscope (Zeiss~ .A~ciovert 100) Cell: Jurkat T cell Reagents: RPMI Medium 1640 (GIBCO BRL, Cat. #31800-014) Fetal Bovirle~:Serum'(HyClone, Cat. #5H30070.03, Lot #AGL7258) 2-mercaptoetlianol (GIBCO BRL, Cat. #21985-023, SxlO-2 M) Culture media: 90% RPMI + 10% fetal bovine serum +
2-mercaptoethanol (5x10-S M) , _ ~ ~ ' Disposable sterile syringe filters. (0.2 m, Corning, Cat. #21052-25) herbal extracts:.:
. , 1. Cordyceps Sinensis Mycelium.' 2. ST 024:

3. ST 044:

4. ST 051:

5. ST 093:

6. ST 117:

7. ST 123:

8. ST 128e 9. ST 134:

10. ST 237:

11. PH~.'906-303503: Complex~mix composed of 4, 6, 7, 10 _73_ 12. P~IY906-284003: Complex mix composed of 4, 6, 7, 10 Procedure: , A. Herbal Extract Prenarataoh 1. Dissolve 1 gram of herbal powder in 10 ml of 80 °C deionized water (neutral pIi) in a polypropylene tube.
2. Incubate the tube at'80 °C water bath for 30 minutes with gentle shaking then centrifuge at 4000 rpril (1500~g) for 5 min to obtain the supernatant.
3. Centrifuge at 11000 rpm (14000~g) for 10 min to collect the supernatant.
4. ZJsing the disposable sterile syringe filter to filter the supernatant.
B. Cell Survival Test 1. Culture Jurkat T cells as described above.
2. Dispense 1 ml of Sx105Ym1 cells per well to 24-well culture plates.
3. Prepare 12 kinds of herbal extract solutions. The extract solutions must be freshly prepared and used~iinmediately.
4. Add 100, 50, 20,10, 5 ,~,1 of each herbal extract solution into the 24 well culture plates to get the five different concentrations: r10, 5, 2, l, 0.5 mg/ml.
5. Culture the cells for 24 h at 37°C W an incubator filled with 5%
C02.
6. Count the number of cells per well. Mix 10 ~,1 of cell solution with 10 ~,l of Trypan blue dye and load irito~~cell counting chamber.
7. Count the four major square areas to calculate the cell number.
(number of cells in 4 areas)/4 x,104 x dilution factor = number of cells per ml 3. Profiling gene~expressiori patterns of Jurkat T cells treated with herbal ... :.. , extracts.
Purpose: Profile the gene expression patterns of Jurkat T cells treated with herbal extracts. A high-density nucleic acid microarray with colorimetric detection system is used:
Apparatus: Heat block (Boekel, lVIode1 110002) Spectrophotometer (Beckman, D~T640) Centrifuge.(KIIBOTA 1910)' Water bath (SI;M.~AMINCO, Model 800) I~ybridization incubator (YI~I DER OIi-800) Heat sealer'(TISH-300, TEW) Reagents: RNAzaITM B (Tel=Test, Cat. #CS-104) Oligotex rriRNA Midi Kit (Qiagen, Hilden, Germany) Hybridization Bags,(GIBCO BRL, Cat. #18278-010) EasiSeal (Hybaid,'Cat: #HBOSSSEZlE) Glass slides (Matsunami, 52214, Japan) Aerosol Resistant Tips (ART'tip) (molecular BIO-Products, Cat. #2139) Random hexamer primer (GIBCO BRL, Cat. #48190-Ol 1) Reverse transcriptase and 5~ buffer (GIBCO BRL, Cat. #18064-014) RNase inhibitor (GIBCO BRL, Cat. #10777-019) Biotin-16-dUTP (Boehringer Mannheim, Cat. #1093070) Dig-11-dUTP (Boehringer Mannheim, Cat. #1558706,) ,~
Blocking powder for hybridization (Boehringer Mannheim, Cat. #1096176) Bovine.seriim albumin, (Sigma, Cat. #A2153) ., ., , 20X SSC (Arizr~sco, Cat.#09185-2-20XPTMSL) SDS (Merck,~Cat.#1'13760) Dextran Sulfatev(Sigma,~ Cat. #D6001) Streptavidin=(3'=galactosidase (GIBCO BRL, Cat. #19536-010) ..
Anti-digoxigeriirl-AP Fab fragments (Boehringer Mannheim, Cat. #1093274,) X-gal (GIBCO BRL, Cat: #15520-018) Malefic acid (Sigma; Cat. #M1125) ~ N-lauroylsarcosiri (Sigma, Cat. #L5777) Fast red TR/AS~MX substrate kit~(PIERCE, Cat. #34034) Polyethylene glycol (Sigma, Cat.#P2139) Reagent Preparation:
1~ hybridization buffer (4X SSC, 0.1% N-lauroylsarcosine, 0.02% SDS, 1% BM
bl~cking reagent) ~ ~ ~ ~ - . ' , 20X SSC ~ ~ ' , - ~16 m I % N-lauroylsarcosine 8 ml IO% SDS '. , . ~ 160 ~.tl BM blocl~ing powder ' ' . ' 0.8 g Ha0 ." . 51"ml total 80 ml Heat to 65°C to dissolve the powder then store at -20°C.
50% PEG-8000 (Polyethylene Glycol) PEG-8000 ~ , 10 g. - .
H20 up ~to ' 20 ml Heat to 65°C to dissolve then autoclave..Aliquot and store at -20°C.
10~ TBS (100 mM Txis, 1:SM'NaCI, pH 7.4) Tris base ~ 12.1 g ' Na.CI ' 87.6 g H20 up to 1000 ml' 120 mM X-gal X-gal' 100 ing.' ' ~: ;,, DMF 2 ml ' Store at -20°C. , ' ' X-gal Substrate Buffer {lmlVI MgCl2, 3 mM K3Fe(CN)6, 3 mM K4Fe(CN)6 in 1X TBS
buffer) . S00 ml lOX TBS/pH7.4 , ' ' S0 ml Potassium Ferrocyanide 633.5 mg Potassium Ferncyariitle 493.9 mg MgCla ~ , 101.6 rng Filter and store at -20C.

BM Blocking Dilution Buffer/pH7.5 (0.1 M malefic acid, 0.15 M NaCI) ' ' ~ .' 100 ml 1M Maleic~Acid SM NaCI ' ~ ~ ' 30 ml Solid NaOH ' '7.5 'g H20 ~ ~ up to 1000 ml 10% Blocking lteagent~ 100 ml Blocking Powder ' 10 g Blocking Dilution Buffer (no tween 20) 100 ml Heat to 70°C then autoclave: Store ~at 4°C.
20% Dextran Sulfate'' S Dextran Sulfate ~ v ° 2 g Ha0 ~- up ao ~ 8 ml Autoclave then store at -20°C.
DEPC-treated water 800 ml Diethyl Pyrocarbonate (store at 4°C) 400 ~l H20 , 800 ml Put in 37°C shaking water bath for 4 hxs and then in 37°C warm room for overnight. Autoclave the solution for 4S minutes or-2S eininutes each for two times.
Procedure:
1 S A. P~e~at~atioh of'Jurkat 'T Cells 1. Thaw one vial of Jurkat T cells. Transfer to 10 ml growth medium. The number of vials to thaw depends on the 'number of test to perform. In general, one vial of cells is needed for 2 herbal extract tests.
2. Re-suspend the cells in SO ml of culture medium.
3. Incubate the cells f~r one day. Add lSOvml of culture medium and divide into two flasks with 100 ml each.
4. Culture the cells for 3 days.
S. Change the medium end distribute 4 flasks with 100 ml of medium each.
6. Culture the cells for ~2 'days. ' 2S 7. Count the cell number. Collect the cells and spin down. Ize-suspend the cell pellet with culture medium~to,Sx1.05 cells per ml and 100 ml per flask.
8. Culture the cells for 3.hrs before adding the herbal extract. ' ~'. ~Ie~bal extract treatment'' 1. Prepare the herbal extract (see herbal extract preparation).
2. According to the growth inhibition concentration of herbal extract determined by the cell survival experiments, c~.lculate the'SO% growth inhibition concentration of each 1 herbal extract. .

_77_ 3. Define the 50% growth inhibition concentration as H. Treat the cell with serial dilution of herbal extracts with the following concentrations: H, H/2.5, H/5, H/10, H/20.
4. Culture the cell for 24.~hrs.
5. Collect cells and count the number of cells. Centrifuge to obtain cell pellet.
6. Wash the cell pellet with 1 XPBS once.
7. Discard the supernatant. The cell pellet is ready for total RNA isolation.
C. Isolation of total R A , ~ .
1. Add 1 ml of RNAzoIT~ B per 10' cells. Homogenize the cell pellet but do not vortex.
2. Add 0.1 ml chloroform per ml of homogenate, cover the samples tightly, shade vigorously for 1 min°,(do not vortex). Place on ice for 15 min.
3. Centrifuge at 12000. rpm (13500~g) at 4°C for 15 min.
4. After centrifugation, tliehoriiogenate develops two phases: a lower blue phenol-chloroform phase and a colorless upper aqueous phase. DNA and proteins are in the interphase and the organic phase. Transfer the aqueous phase to a new tube, add an equal volume of isopiropanol' and store the samples at -80°C. Note. The range of isopropanol additioi~~'ir'~rorn 0.7 tb 1 volume of the aqueous phase solution.
5. Keep the samples at'=~0°C',uritil use: Let the sample completely thaw before centrifuging and mix 2 to 3 times by inverting the tube. Centrifuge samples for 15 min at 13000 rpm' (15000~g). ~ ' 6. Remove the~supernatant'and wash the RNA pellet once with 1 ml of 75%
ethanol.
Centrifuge for 3'rriiii at' 13~OOO ipm (15000Xg) and at 4°C.
7. Discard the~supernataiit. I~ry the pellet under vacuum for 1 min. Note. Do not Iet the RNA pellet diy coriipletely.rIt will greatly decrease its solubility.
8. Dissolve the RNA pellet iri X50-100 p,1 of diethylpyrocarbonate (DEPC) -treated water by pipetting. Note. ~If tlie'pellet is hard to dissolve, incubating the pellet for 10 - 15 min at 60°C may help': 5:, .,, ; : , , , , ~~ a v':
9. Measure absorbance ~~.~ 260'iun ~(Aiso) and 280nm (A28o) with a spectrophotometer.
Concentration analysis:' OD26o x 40' ng/p,l X dilution factor = total RNA
(ng/~,l).
D. Isolation ofRolv A~+ mRNA fYOm total RNA p~
1. Determine the amount of starting RNA and the appropriate volume of Buffer OBB and Oligotex Suspension ~solutiori to be added in the RNA solution according to the Table 17.

_78_ Table 17. Buffer amounts for Oligotex mRNA Spin-Column Protocol.
Total RNA Add RNase-free Buffer Oligotex Prep vaster to: (~,l)OBB Suspension size (~,l) (~,1) 20 ~,g 100 100 6 Mini 0.25 mg 250 . . ~ 250 . 15 Mini 0.25-0.5 mg 500 500 30 Midi 0.5-0.75 mg 500 500 45 Midi 0.75-1 mg 500 : 500 55 Midi The following procedures are based on using 500 ~,g total RNA as an example.
2. Add 500 ~.1 of 2X Binding Buffer and 30 ~,l Oligotex Suspension to the total RNA
sample. Mix the contents thoroughly by flipping the tube.
3. Incubate the sample for 10 min at 70°C.
4. Incubate for. 20 rriin, at room.temperature.
5. Centrifuge for 2 min -at maximum speed (14000 to 18000Xg) and aspirate the supernatant. ._, ;.. ~ .
6. Re-suspend the pellet in 400 ~,1 of Wash Buffer OW2 and transfer onto spin column and centrifuge the spin column for '1 min.
7. Wash with 400 ~1 of ~OW2 and centrifuge as above.
8. Add 20 ~,l of preheated (70°C) Elution Buffer onto the column and re-suspend the resin. Close the microcentrifuge tube.
i..., , 9. Put the spin column with 1.5 ml microcentrifuge tube at 70°C for 3 min.
10. Centrifuge the c~oluniii at maximum speed for 2 min at room temperature.
11. Elute again. (repeat step 8 to ~1~0 to get better yield) .
E. cDNA Labelih~
1. Mix 2~,g mRNA,~ ~1, ~.l of control plants mRNA for single color label (Hat22: 1 X 109, Rbcl: 5 X 1 O8, Ga4: 1 X 108, Rca: 5 X 107, Asal : 1 X 107, Atps: 5 X 1 O6 molecule/~,1), 6 ~,1 of . , ,. .F;: . . . . .
~50 mM random hexamer and DEPC-H20 to 28.88 ~,1 final volume. For dual-color mode, use 2 ~.g of ~mRNA each ~in Biotin or Dig labeling and individual addition of control plants mRNA~: .1~. biotin labeling: Hat22: 1 X 1 O8, Rbcl: 5 X 107, Ga4: 2 X 107, Rca:
1 X 107, Asal : ~1 X 107;' Atps:. fX 107, Hat4: 1 X 107/x.1. 2. Dig labeling:
Hat22: 1 X 107, Rbcl:
..
1 X 107, Ga4: 1 X 107 Rca: 1 X.107, Asal : 1X 108, Atps: SX 107, Hat4: 2X
107/x,1.
2. Denature for IO min at 70°C, then chill quickly in ice for 5 min.
3. Add 10 ~,l of 5 X first strand buffer, 5 ~.1 of 0.1 M DTT, 1 ~,l of 25 mM
dATP, dCTP, dGTP mixture 1 ~.1 of 2 mM dTTP , 2 ~,l of 1 mM biotin-16-dUTP, or Dig-11-dUTP
(1 mM), 0.63 ~,l of 40 ~ IJ/~1- RNAsin and 1.5 p,1 of Superscript II (reverse transcriptase, GIBCO BRL)(200'L1/~.l).
4. Mix well and incubate for 10 min at 25°C, then for 90 min at 42°C.
5. Stop the reaction for 5 min at 94°C.
6. Add 5.5 ~.l of 3 M NaOH for 30 min at 50°C.
7. Add 5.5 ~,1 of 3 M, CH3COOH for 30 min at 50°C.
8. Precipitate the labeled cDNA by adding 34 ~,1 of water, 50 ~.1 of 7.5 M
anunonia acetate, 10 ~,g of linear polyacrylamide as carrier and 380 ~.1 of absolute alcohol.
9. Incubate the sample for 30 min at -80°C. Centrifuge at 13000 rpm for 15 min.
10. Wash the pellet with 1 ml of 70% ethanol and centrifuge at 13000rpm for 5 min.
11. Dissolve the pellet in 36 ~,1 of autoclaved HZO. For dual color, combine two labeled cDNA together.
F. Arrav Hvbridization 1. The filter membrane carrying the 9600 EST PCR products is pre-hybridized in 5 ml of lx hybridization buffer,~(4X SSC, 0.1% N-lauroylsarcosine, 0.02% SDS, 1% BM
blocking reagent (Boehringer Mannheim)), and 50 ~.g/ml salmon sperm DNA (GIBCO
BRL) at 63°C for 1.5 hours. Note. You can prepare 80 ml of 1 ~
hybridization buffer and store it at -20°C: Thaw the buffer at 60°C before use.
2. Stick one side of adhesive EasiSeal~ square to a clean glass slide and place the pre-hybridized membrane in the center of the square with the spots facing up.
3. Mix the probe with 2 ~,I of poly-d(A)10 (10 ~.gl~.l) and 2 ~,1 of human Cot-1 DNA (10 ~,g/~.1) (GIBCO BRL) and'40 ~.1 of 2~ hybridization buffer to 80 ~.1 final volume.
4. Denature the probe iizixture at 95°C for 5 min and then cool on ice.
5. Seal the filter membrane with the probe solution in the hybridization bag.
6. Incubate at 95°C for 5~inin~and then at 63°C for 12-16 h (overnight).
,. .
7. Wash the filter membrane twice with 5 ml of 2X SSC, 0.1% SDS for 5 min at room temperature. ' ',-8. Wash three times for l5~inin each with 5 ml of 0.1 X SSC, 0.1% SDS at 63°C.
9. Block the filter membrane with 5 ml of 1% BM blocking reagent containing 2%
dextran sulfate at roorri temperature for 1 h.
10. Incubate with 5, ml~' mixture containing 700X diluted Streptavidin-(3-galactosidase (1.38U/ml, enzyme activity)(GIBCO BRL), 10000 diluted anti-Digoxigenin-alkaline phosphatase (0.07SLT/rizl,.,enzyme activity)(Boehringer Mannheim), 4%
polyethylene glycol 8000 (Sigma); and .0:3% BSA in 1 X TBS buffer at room temperature for 2 hours.
Note. This formula,is~for dhal-color mode. For single color mode, anti-Dig-AP
is not needed and the incubation time can be reduced to 1 hour.
S 11. Wash with 1 X TBS buffer three times for 5 minutes each.
12. Freshly prepare X=gale, substrate solution (1.2 mM X-gal; 1mM MgCl2, 3 mM
K3Fe(CN)6, 3 mM K4Fe(CN)6 in 1 X TBS buffer) by mixing SO p.1 of 120 mM X-Gal and S ml of X-Gal substrate buffer. Immerse the filter membrane in the X-gal substrate solution for 4S min at 37°C with gentle shaking.

13. Wash with lx TBS. ~ . - .

14. Dual color development: stain the membrane with S ml of Fast red TR/naphthol AS-MXsubstrate(Pierce;, Rdckford, IL) at room temperature for 30 minutes with gentle shaking.
1S. Wash with dexonized water. Stop the reaction with 1X PBS containing 20 mM
EDTA
for 20 min. ~ ' ' 16. Air dry the filter membrane: , Results. ~ . . ; . ,~ .
1. Determine the growth irihiliition concentrations of herbal extracts in cell cultures.
Each herbal extract has~different cellular toxicity, thus it is necessary to determine the growth inhibition concentration of every herbal medicine before treating the cells. Five serial dilutions of herbal extrac'tv(lO;vS, 2; ~1, O.S mg/ml) were added to Sx105/ml cultured cells and incubated fo'r 24 hours in an incubator at 37°C with S% C02. The numbers of survival cells at different concentrations~of herbal 'extracts are shown in Table. 18.

Table 18. Survival Cell Number at Different Concentrations of Herbal Extracts experimental concentration no.x105/ml 10 5 2 1 0.5 No High Low cone.
rng/mlmg/mlmg/mlmg/mlmg/mldrugcone. (mg/ml) (mg/ml) 1 Cordyceps sinensis8.4' 11:911.59.2 9.0 12.410 1 mycelium 2 ST024 ' 4.2 ' 10 7.5 10 10 10 1 8.7 3 ST044 .5.98.4 9.9 9.4 5 0.5 ~

4 STO51 ~ ' - - 1.7 5.4 0.5 0.05 S 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 S 0.5 8 ST128 3.5 7.7 7.9 7.7 8.3 5 0.5 9 ST134 2.9 6.1 11.29.6 9.8 5 0.5 ST237 - - 2.5 6.6 8.7 1 0.1 11 PHY906-303503 4.5 4.9 8.2 1 0.1 12 PHY906-284003 - = 3.8 5.7 7.6 1 0.1 ~ ' Note: The original 10 cell number /ml.
was 5x The number increased to 10x10 /ml after 24h incubation. "-" indicates all. dead cells..
5 , The cell number without herbal extract addition doubled after 24 hours incubation. On the other hand, the number,of survival cells varies with different herbal medicine treatments.
We chose the 50% growth inhibition concentration (ICSO) as the high concentration and one-tenth of it as the low coricentratiori. In order to maintain consistence of the Jurkat T cell line, a ;.
10 cell banking system was established. In the cell bank, a total of 100 vials of cells (10 million cells per vial) were. frozen in .a.-150°C freezer.
B. Molecular classification of herbal medicines by nucleic acid microarray analysis. ~ ' Analysis of 3 single-element herbal medicines: Three single-element herbal medicines, Cordyceps Siriensis Mycelium (CSM), ST024, and ST117 were used to treat the cell cultures as described in the methods"section. Gene expression measurements were performed by using microarrays of 13824 cD~tA.,fragmerlts each representing a distinct human transcript. For the data analyses, gene spots of l~gh~ data quality were selected. The selection was based on signal to background ratio greatervtliaw2.5 or the coefficient of variation (CV) of spot area smaller than 10%. All the data sets were normalized with the control cells, which received no herbal treatments. The spot intensity'wa's rounded up to 10 for those intensities that were less than 10.
Based on the selectioncriteria; a total of 492 genes with differential expression ratio greater than 1.5 were ~selecteii foreclusteranalysis. These data pre-processing procedures were performed by the prograrii ~"D'ataExtract" and "Ratio2" developed in-house.
These 492 genes'were'clu'ster analyzed by the average-linkage method. The distance between genes is used as~the linear correlation coefficient or resemblance coefficient. The cluster analysis programs; Cluster and TreeView, were based on hierarchical clustering method and were written by Dr. Michael Eisen of Stanford University (Eisen, 1999, Eisen et al., 1998).
The results are shown in Figure 1.2. From Figure 12C, one can clearly identify that three different herbal medicines ~of high and low concentrations are each clustered together. For instance, CSM-L is closer.to:CSM-I3 and less.similar to ST024 or ST117 in the clustering tree.
A different clustering algorithm, the self organizing map algorithm, which is based on non-hierarchical method, yields 'thesame results (data not shown). From the clustering results . ~; , shown in Figures ~12A &' ~1~3A~vseveral' features are noted. (1) 4 genes were up regulated by ST117 treatment but down~regul'ated by other herbal treatments (Figure 12B).
(2) 34 genes were down regulated by CSM~~treatment but up regulated by others (Figure 13B).
(3) 2 genes were up regulated by all the three 'herbal treatments, one is Malic enzyme 2 and the other one is an anonymous gene (clorie'IlD: 328351) (Figure 13C). (4) 12 genes were highly induced by the ,~;
high concentration treatmerit~arid less induced by the low concentration treatment in all the three herbal medicines (Fig.r 13D). ' ' ' Analysis of 2 ~re~arations'of mufti-element herbal medicines. Two batches of 1=Iuang Chin Tang, PHY906-303503(#1.1) & PHY906-284003 (#12), each with low and high concentrations were used to treat the cell cultures in three independent experiments. The gene expression profiles were ~acqi~ired with microarrays of 9600 non-redundant cDNA elements.
After the data pre-processing procedures as described above about 5000 genes were selected x;. , , for the subsequent data analysis.TThere were 3 repeats for each herbal treatment. For data analysis, we use a modified methof;based on the one reported by Slonim et al.
(Slonim, 1999).
The following algorithm is designed to search for the candidate marker genes that have high differential expression ratios ~liut low deviation in the three repeats. We designate a P(i) value to account for the gene' i with 'tlie aforementioned features.
P(i) = square root of~(E(~m:-p.~)2)~-(6~+Esm) . r.
: Mean expression ~levels.iri three repeat experiments for herbal treated cells (p,m) or untreated control, cells (~,~): ~'. , ~ .

cr : Standard deviation of the expression levels in three repeat experiments for herbal treated cells (6m) or untreated control cells (6~).
We.calculated the P(i) value for each gene and selected 500 genes with the highest ..
scores as candidate genes; for cluster analysis (Figure 14). The values of each gene were averaged over the:3 repeats: As shown in Figure 14B, two different concentrations of #12 are clustered together (12-H c~ 12-L). The 1>igher concentration of the #11 preparation is closer to the #12 preparation cluster than the~lower concentration of #11 preparation.
However, all these clusters have sirnilax resemblance coefficient (distance between clusters) compared with the tree shown in Figure 12. These results suggest that the gene expression profiles of #11 and #12 preparations ofHuang ChinTang are similar. The results are justified based on the fact that these two preparations are' based on'the same herbal medicine mix.
Several features are rioted in the expression profiles illustrated in Figures 14A & 15.
The averaged gene expression levels are shown in Figure 15A The Boxl encloses genes that were down regulated in #11'~-L treated cells but up-regulated in others. These genes include 2 tRNA synthetase (isoleucirie'aii~ methion), RNA polymerase II polypeptide B
(Clone ID
42020), KIAA0212 gener(Clorle ~~D 310497, containing ATP/GTP-binding site motif A), and KTAA0577 (Clone'ID,29263,vATP-dependent RNA helicase). It is interesting to note that 3 out of the 6 genes were involved~'iii tlie~RNA replication. Box2 encloses the genes that were up regulated by all the #1 l arid #12 treatments. Box3 encloses the genes which showed no response by #11-L treatment but were down regulated by the others. Box4 encloses the genes that were highly repre'ssed'by low concentration herbal treatment but were less repressed by high concentration herbal treatrrierit. Finally, in Boxl and Box3, the expression profiles of #11 treated cells are different~from the profiles generated by the other 3 treatments. This result is consistent with the fincting'tiepicted in Figure 14B. ' Combining the dataysets of the gene eXpression profiles of the 3 single-elements and the ', , , 2 preparations of mufti-elernerit herbal medicines together, a couple features are noted as an illustration. The KIAA0212.gene ~(Clorie ID 310497, containing ATP/GTP-binding site motif A) was highly induced by all the'high concentration herbal treatments except that it was only mildly induced by the #11-Iand-the CSM treatments. Two genes, an anonymous gene, (Clone ID 510908) and Proteasoriie chain 7 precursor (Clone ~ 70088) were highly up-regulated on all the treatments except~dowxi-regulated by the CSM treatment.

We next wdrked~ori the ciux to 'cluster analyze the gene expression profiles of the 5 .,.
different types of herbal.medicine treatments. The data pre-processing procedures were performed as aforementioned and~500 genes were selected for cluster analysis.
A hierarchical clustering was performed by the prograrri "Cluster'° described above.
The hierarchical tree was cut at the position where the 'range of distance between clusters is the greatest (Romesburg, 1989) and the result is shown ire Figure 16A. The 3 single-element herbal medicines, CSM, ST024, and ST117 are clustered together. Out of the 2 different batches of the mufti-element herbal medicine, #12-H, #12-L and #11H are clustered together and the #11-L
stands by itself.
The result suggests that higher similarity exists between the #11 and #12 as compared with that of CSM, ST024, and ST117: In order to better classify the different herbal medicines, the data analysis algorithm was improved.by~standardizing all the data sets so that the expression level of each gene across thevdifferent data sets has zero-mean and unit=variance (Tavazoie et al., 1999; Chen et al., 1999). ThYS yields the transformed variables:
Y
1 s x; _ (xi-pX)~ a~X
mean expression levels in the' data set 6X: standard,deviation of the expression levels in the data set x; : un-transformed gene expression level ~; : transformed gene ~eXpression level After staridardizingtthe 'dataYset;, #1 l and #12 are clustered together as shown in Figure ., 16B. The ST024 and ST1'l7~are clustered together and the CSM is in an independent cluster.
Furthermore, the clustering also 'suggests that CSM is more similar to #11 and #12 than to the ST024 and ST117. Another clustering algorithm, self organizing maps, was performed with the same standardized data sets and yields the same result as the hierarchical clustering (Figure 16C).
Class predictors for discriminating #11 and #12 herbal treated expression a~rofiles- The above cluster analyses for.the #11 and #12 show that they are similar and further classification is difficult by the hierarchical~clusteririg or self organizing maps methods with the data set containing the 500 genes of the highest P(i) values. We then modified the algorithm to select genes with larger expression ratio difference between #1 l and #12 herbal treated cells, but smaller variation in the two Herbal treated cells. The T(i) value is defined to score this feature as following: ~ ~ ~ ~ .. . ' T(i) _ log('~,yl) -= log(Wa) ~ (611'612) p,: Mean expression ratios din three time experiments for #11 treated cells (p.11) or #12 r.
treated cells (~12) v ' ~~ ' o' : Standard deviation of'the expression ratios for #11 treated cells (611) or #12 treated cells (~11) We calculated the T,(i), ealue for each gene and selected 50 genes with the highest .~ a scores as class predictors~(Figure 17): 18 genes were up-regulated by the #l l~treatment and down-regulated by'the #l2.treatment. The rest of the genes were up-regulated by the #12 treatment and down-regulated by the #11 treatment. We then used these class predictors to classify two test herbal preparations based on a modified method described by Ciolub et al., 1999. . . , Two different,batches of°Huang Chin Tang preparations, PHY010401 (#16) ~
PHY010402 (#17) were obtained from Sun Ten Pharmaceutical Co. and were used for the class prediction test. The.'gene expression profiles of #16 and #17 preparations were normalized with the expression profile of the untreated control cells and standardized with. the class predictors. Each predictor g; votes for either #11 or #12 herbal preparation depending on whether its expression level X; .is closer~to #11 or #12. The vote for each gene is given by v; _ ~xi - (I~m+~.c)~2I~ where ; ' ~.: Mean expression'ratio: iri thiee~ repeat experiments for #11 (x,11) or #12 herbal treated cells (~,lz) The average votes~~Tl1 and~Vy2 were collected from the predictor genes correlated with the predictor on #11 and #~12,~respectively. The prediction strength (PS) reflects the margin of victory and it was defined as PS = (Vll-~la)~ (Vll+Vlz). If the PS was greater than 0, it indicated that the herbal preparation was more similar to #11 and less similar to #12. The results obtained from the analyses on,#16 and #17 indicated that #16-H was similar to #11 (PS=0.1) and #16-L, #17-H; arid #I7-L were similar to #12 (PS=-0.29, -0.21 and -0.2, respectively). Based on the information of #16 and #17 preparations, this test failed to correctly identify #16-L asmore similar to #11.
Discussion. ' ~. . . .
Characteristic ec~ ne. expression profiles for herbal medicines treated cells.
The predictor genes are.selected from the differentially expressed genes in two herbal treated cells. These genes represent the cellular responses to the herbal medicine treatments. In this study, we have identified some interesting genes based on their responses across different herbal treatments (Figures 13, 15, and 17). These genes are valuable assets in studying the signaling pathways of cells in response to the herbal stimulation and in deciphering the molecular pharmacological mechanism of herbal medicines.
Classification bf herbal rrieelicines by nucleic acid microarra,~analvsis. In surrnnary, a two-step classification proced~e ~is proposed. An initial classification procedure based on the standardized data sets and the clustering algorithms is performed and followed by a final classification procedure with'the class predictors. All the procedures can be integrated in a computer program. In these preliW inary studies, all the genes have the same contribution for classification. When the data sets are large enough, the weight for each gene (or predictor) can be acquired from the linear correlation coefficient (Golub et al., 1,999, Chen et al., 1999).
The #11-L failed to be clustered with #11-H by significant association. A
similar preparation of #11, the #I6 preparation yielded the same results. It was interesting to discover that no matter what clustering algorithms were applied, the #11 and the #16 preparations did not yield the expected result's. Even with independent experiments, the results remained the same. The reason behind the~~failure will be investigated with detailed information of #11 and #16 preparations.
~uality control and analysis in microarra,~ystem. The quality of acquired microarray data and the 'choice of the statistical analysis methods are both important factors for achieving meaningful results. We have recognized that the variation among arrays contributes to errors in measuring gene expression levels. Based on the data in this report, we have found that for every herbal preparation, the high concentration treated expression profiles always cluster with its lower concentrationcounterpart (Figures 16B and 16C) and we could classify the STI 17, ST024., CSM and Huang Chin~'Tang with two different clustering methods. In the past three ' months, the array quality has'been~improved to have less than 7% CV. We have also set up a standard procedure for assessing the:quality of every batch of arrays fabricated in the lab. All _87-these experimental findimgs:and improvements in the microarray technologies suggest that classification and characterization of the Chinese herbal medicines by microarray system are feasible.
References for Exam~le~l5: v Bertucci-F; Bernard-K; Loriod-B:; Chang-YC; Granjeaud-S; Birnbaum-D; Nguyen-C;
Peck-K;
Jordan-BR (1999) Sensitivity issues. imDNA array-based expression measurements and performance of nylon microarrays'for small samples Human Mol. Genetics 8(9):
1715-1722.
Bittner-L, Trent-J, Meltaer-P~(1999) Data analysis and integration: of steps and arrows. Nature Genet. 22, 213-215 . ..
Brown-PO; Botstein-D, (1999j' Exploring the new world of the genome with DNA
microarrays.
Nature genetics 21 (1) suppleirient; 33-37.
Chen-JJ; Wu-R; Yang=P~;~ Fluang-JY;v Sher-YP; Han-MH; Kao-WC; Lee-PJ; Chiu-TF;
Chang-F; Chu-YW; Wu-CW; Peck-~~(1~998)~expression patterns and isolating differentially expressed genes by cDNA microarray,systern'with colorimetry detection. Genomics. 51: 313-24.
Chen-Y, Bittner-M, Dougherty-ER (1999) Issues associated 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-1VI; Clieii=YlVleltzer-P Trent -JM (1999) Expression profiling using b, cDNA microarrays.~Natur~'gerietics. 21 (1) supplements 10-14.
Eisen-M (1999) Cluster and'~Treeview manual.~(rana.stanford.edu/software) . ..., , s.R .
~:, .r, y,, ,. .. . .
Eisen-M, Spellman-PT, Brbwn=PO, Botstein-D. (1998) Cluster analysis and display of gene-wide expression patterms. P'roc. IsTatl. Acad. Sci. LTSA 99:14863-14868 ' . ~ ' Golub-TR, Slonim=DK; 7Camayo-P, Hoard-C, Gaasenbeek-M, Mesirov-JP, Coller-I~, Loh-ML, Downing-JR, Caligiuri-MA,~B~loomfield-CD, and Lander-ES (1999) Molecular classification _88_ of cancer: class discovery' arid: class prediction by gene expression monitoring. Science ~ct 15:
531-537 . . , Larder-ES (1999) Array of~hbpe. Nature'genetics 21 (1) supplement, 3-4.
.:
Romesburg-HC (1989) Cluster analysis for researchers. Chapter 16: How to make classifications. P203-216, I~rieger Publishing Co. Malabar, Florida, LTSA
Slonim-DK, Tamayo-P, Mesirov-JP,.Golub-TR, Larder-ES (1999) Class prediction and discovery using gene expression data. ~(www.genome.wi.mit.edu/MPR) Tavazole-S, Hughes-JDCampbell=MJ, Cho-RJ, Church-GM. (1999) Systemic determination of genetic network architecture: Nature genetic 22: 281-285 Example 16. Identify characteristic gene expression profiles induced by an herbal medicine . , , , ..
As stated in Example 15; the prescription of the Chinese herbal medicine Scute and Licorice combination (Huang Chin 'Tang) stops diarrhea, relieves spasms and clears fever. The ingredients of Huang Chiri 'Tang are Scute, Peony, Licorice and Jujube. In this study we used the nucleic acid microaxray technology to study the gene expression profiles induced by herbal medicines in mammalian cells: To investigate the characteristic expression profiles induced by the Huang Chin Tang, Jurkat'.fi cells~were treated with 5 batches of Huang Chin Tang (PHY01040; #16, PH3~'010402; #17, PHY03061; #18, PHY03062; #19 and PHY02231;
#20 obtained from Surf Ten Pharmaceutical Co.) by 5 concentrations (1/2, 1/2.5, 1/5, 1/10, and 1/20 of ICso) Nucleic Acid microarray with a two color detection method was employed to measure the expression profiles. The mRNA extracted from herbal treated cells was labeled with digoxigenin and the mRNA extracted from untreated cells was labeled with biotin. Arrays of 9600 features were erriployed arid the procedures described by Chen et al.
(Genomics, 51, 313-324, 1998) were adopted in the''experiments. For data pre-processing, only array spots of high data quality were selected: rThe selection was based on signal to background ratio greater than 2.5 and 1.5X differential eXpressiori ratio. Ey these criteria, 1081 genes were selected for further statistical analysis:~Non-hierarchical cluster analysis programs such as the Genecluster program developed in Massachusetts Institute of Technology (Tamayo et al., 1999) was employed to categorize the expression profiles. The Genecluster program is based on self organizing map (SOM) principle. A°6X4 SOM clustering of expression profiles are shown in Figure 18A. The ol.etails of geneexpression profiles for the selected clusters are shown in Figure 18B. In these clusters, °clusters 3 and 20 (labeled c3 and c20) were selected for that the gene expression levels increase with higher herbal concentrations. Similarly, clusters 5 and 9 for that the gene expression levels decrease with higher herbal concentrations. Cluster 23 collects genes whose expression levels were up regulated compared with that in untreated cells and cluster 0 for down regulated genes. These expression profile clusters are further condensed into two major groups A & ~B. Group A collects genes up regulated by herbal treatment and Group B collects.genes down regulated by herbal treatment. The expression profiles in Group A & B form the basis of a characteristic expression data set for an herbal preparation. The same procedures were repeated for 5 different batches of Huang Chin Tang, and 952 genes were selected to establish tliercliaracteristic expression profile database of the Huang Chin Tang. ' T
As shown in Figure 1.9,'bythe aforementioned procedures, a gene can be categorized as Group A, B or none (non-A and non-B) and its expression profile can be represented by l, -l, and 0 respectively. The niimlier.of different gene expression profiles between batch #1 and batch #2 are 3 in Group°A.(Gene 6,'7, and 8) and 2 in Group B (Gene 15 &16). By the same principle, the number of different expression profiles between batch #1 and #3 are 10 in Group A and B and the number isvl,l' between batch #2 and batch #3. These numbers indicate that batch #1 and #2 are more. similar than batch #3. This principle was applied to classify 5 different hatches of herbal, preparation's. The following algorithm is designed to calculate the distance between a pair ofiieilial preparation batches, i and j.
d;; = E ~(X;, X;) ~~ ~(Harriming distance) The gene X in i batch of preparation.is assigned to Group A, B or none if x; ~ x; ; s(x;~"x;) =,1, ~ ; ..
if x; = X; ; s~x;,.xj ~= o. , .
We calculated all the d;;~ value between pairs of herbal preparations for cluster analysis.
The analysis programs, Kitscli'Gluster was based on hierarchical clustering principle and was written by Dr. 3oseph Felsenstein of Washington University (http://evolution.genetics.washirigton.edu/phylip.html). From the Hamming distance table (Figure 20), one can clearly ieleritify that the shortest distance lie between batch #17 and batch #18 and that batch #17 is siriiilar to #18. Batch #16 also similar with. batch #17 and #18 but batch #19 is dissimilar to~.the rest~of~batches. The results were confirmed by HPLC analyses as described below. . ' .' By HPLC, the chemical 'composition of the 5 batches of herbal preparations was analyzed.
Four major peaks (BG, B; Gly, and Pf) in the chromatograms were selected for statistical analyses. Two additional.para'rneters, BG+B and BGB, were included in plotting the 6-coordinate radar graph as shown in Figure. 21. The distance on each coordinate is the integrated intensity of that particular.cliemical constituent in the chromatogram. In general, #16, #1~7 and #18 were similar in their constituent content (liaicalin and baicalein) of Scutellariae Radix, which are within 33:55-36:08; while the amount of the same constituents are higher in #19 and #20 (42.49 and 44.96, respectively). The resemblance of #16, #17, and #18 can be seen from the coincident radar plots in Figure 21B.
. ;,...
In order to identify the ~unl~nown herbal medicine based on the characteristic expression profile database established ~as described as above, Jurkat T cells were treated with a tester sample #17 in 5 concentrations to set up the characteristic expression data set for the tester.
The Hamming. distances between the tester and each of the data sets (#16, #17, #18, #19 and #20) in the characteristic expre~ssiori;database were calculated and the scores are: #16: 502, #17: 405, #18: 402; #19:'699, arid #20:531. These data show that the tester is most similar to #17 having a lowest. Ha ~rrirning distance score of 405. The example demonstrates that this invention teaches a method'to'identify'unknown herbal medicine based on the gene expression profiles induced by the herbal~riiedicine iri mammalian cells. The identity of the unknown herbal medicine can be inferred by aligning the characteristic expression profiles with a collection of characteristicveXpression profiles of herbal medicines in an HBR
Array.
Based on the characte~istic~expression database, marker genes and signature expression profiles can be deduced fort'air herbal medicine for studying its pharmacological mechanisms and for optimizing the formulation of aAcomplex herbal preparation. For this example, 5 different batches of IIuang'Clun Taiig'preparatioris (#16, #17, #18, #19 and #20) were obtained from Sun Ten 1'laarmaceuticalvCo.~ and a characteristic expression profile database was constructed based ~on aforementioned procedures. For each gene, the consistency of expression profiles in the database wa's "scored by the coefficient of variation (CV
value):
CV = ~ / (E ~,; /n) p.;: Mean expression ratios for.#i treated cells.
n : Number of the data set, i1 --~5 in this case.
. .
6 : Standard deviation of tie .expression ratios for #16, #17, #18, #19 and #20.

Since the CV reflects the variation of data, the marker genes for an herbal medicine were selected based on the CV'score. The top SO genes with the minimum CV scores were selected.
Figure 22 shows 2S marker, genes :with up regulated signature profiles and 2S
marker genes S with down regulated signature.profiles for Huang Chin Tang.
The characteristic expression profile database can be used to infer the expression profiles of individual chemical constituents in a~ mixture as complex as an herbal medicine if the amount of the chemical constituents can be semi-quantitatively determined. In this example, the chemical composition of.an herbal medicine is determined by high performance liquid chromatography. The integrated intensities of 4 chemical constituents in five batches of Huang Chin Tang preparation were quantified by HPLC analysis. The gene expression ratios for each batch of herbal preparation were calculated by taking the median of the expression ratios induced by S concentrations 'of herbal preparation. The correlation between a constituent and a gene expression profile was quantified by the~Pearson correlation coefficient.
The Pearson 1S correlation coefficient forvgeme x~.and the constituent y is:
R = (1/n) E(x;-~,X)( Ysvl~v)'/ sX ay , i= 1 to n n : Number of the herbal preparation,, n = S in this case.
~,X: Mean expression ratios in ,five herbal preparation for gene x.
~,y: Mean integrated intensity in five batches of herbal preparation for constituent y.
x;: Gene expression ratios in #i herbal preparation for gene x.
y;: Integrated intensity.in #i hezbal preparation for constituent y 6: Standard deviation of the expression ratios (ax) or integrated intensities (6Y) for five herbal preparations. . ' 2S Several genes whose expression levels highly correlated (with ~R~>0.99) with the amount of . ;,: ,~
chemical constituents in Huar~g Chin Tang were identified for each constituent. For example, the R value between the gene (clonelD: 67185) and Glycyrrhizin was 0.998 (Figure 23A). One the other hand, the gene ~(clomelD:.344720) whose expression levels increase with the decrease of VVogonin(WG) has an R value of -0.997 (Figure 23B). In addition to the above two examples, 191 and 170 genes were highly correlated with individual constituents with R value > 0.9 and R value < -0.9, respectively. For instance, 17 and 18 genes were positively and negatively, respectively, correlated with Albiflorin (Af) (Figure 24). This example teaches a method to profile gene expressiomfor individual constituents in a mixture without isolating them to perform the expression' analyses one constituent by another.
References for Example 16::
US Patent documents , , Stoughton-Roland and Friend-SH. USP#~ 5965352: Methods for identifying pathways of drug action ~ ~ . , , Brown-PO and Shalon-TD.LJSP#5807522: Methods for fabricating rnicroarrays of biological samples ' ~ ~...
Lockhart-DJ, Brown-EL, Wong;,GG,°Chee-MS, and Gingeras-TR. USP#6040138:
Expression monitoring by hybridization to.'liigli~density oligonucleotide arrays Ladunga-I. USP#5987390:~1Vlethods.and systems for identification of protein classes . . ~ .' . ~ . . ' Mahant-S, Shivaling-5,~-'.Vivek-G. USP#5951711: Method and device for determining hamming distance between two ~iulti=bit digital words Fo~eign.Patent documents Brown-P~ and Shalon-TID~,EP#913485A1: Method and apparatus for fabricating microarrays of biological samples . - .. : .:.~., . . ..
y , . ° , ,°, ~~
Other Publications Bertucci-F; Bernard-K; Loriod-B; Chang-YC; Granjeaud-S; Birnbaum-D; Nguyen-C;
Peck-K;
Jordan-BR (1999) Sensitivityl;issues in DNA array-based expression measurements and performance of nylon microairays 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; .Botsteiri-D (1999)rExploring 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, Douglierty-ER,(1999) Issues associated 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; Cherl-Y; Meltzar-P Trent -JM (1999) Expression profiling using cDNA microarrays. Nature genet. 21 (1) supplement, 10-14.
Eisen-M (1999) Cluster and ~'reeviev~ manual.
(ftp:l/rana.stanford.edu/software) Eisen-M, Spellman-PT, Brovv~i-PO, Botsteiri-D. (1998) Cluster analysis and display of gene-wide expression patterns. Proc: Natl. Acad. Sci. USA 99:14863-14868 Golub-TR, Slonim-DK, Tamay~-P, Huard-C, Gaasenbeek-M, Mesirov-JP, Coller-H, Loh-ML, Downing-JR, Caligiuri-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) Iiiterpretiii'g~patterris of gene expression with self organizing maps: Methods and application'to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA
99:2907-2912 . i 6 .
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, Walthaxiz-M, .Smith=LH, Lee-JK, Tanabe-L, Kohn-KW, Reinhold-WC, Myers-TG, 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 caucer. Nature genet. 24: 236=44: .
Slonirn-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-MT, Cho-RJ, Church-GM. (1999) Systemic determination of genetic network architecture. Nature genet. 22: 281-285 Example 17. Identification of the biores~onses and the signature gene herlbal composition.
To further investigate the expression profiles induced by the.Huang Chin Tang, Jurkat T
cells were treated with Huang hiii Tamg (PHY906#2 obtained from Sun Ten Pharmaceutical Co. Taiwan) by 5 concentrations (1/20, 1/10, 1/5, 1/2.5, and 1 of ICSO).
Nucleic acid microarray with dual-color detection was employed to measure the expression profiles. The mRNA extracted from herbal'treated and untreated cells were labeled with Biotin-16-dUTP
and Dig-11-dUTP9 respectively.~A control~group was established by using mRNA
extracted from untreated cells and labeled the mRNA with biotin and dig respectively in equal proportions. Five sample groups with different concentrations of herbal treatment and one control group were employed for the study by the procedures described by Chen et al. (1998) with minor modifications. For data pre-processing, only array spots of high data quality were selected: The selection criteria,were spots with signal to background ratio greater than 2.5-fold and more than 1.5-fold in differential expression ratio: By these criteria, 1044 genes were .. ~ v ~~,.;.
selected for further statistical: analysis. The gene expression profiles of the control group were highly correlated with only 4.8 genes listed as statistical outliers that lie beyond the 2-fold differential expression range (Figure 25A). For the sample group, many differentially expressed genes are evident 'as illustrated in Figure 25B. The number of genes whose differential expression 'ratio greater than 2-fold 'increases with concentration of herbal treatment as shown in Figure 25C. These results~prove that the identified differential expressed genes are truly iritiuc~ed by Huang Chin Tang treatments.
To identify the genes that were specifically induced by PHY906 (signature genes for PHY906), the expression profiles were clustered by a non-hierarchical cluster analysis programs "GeneCluster" developeci'by Tamayo et al., 1999. The computer program is based on self organizing map (SOM) principle and the clusters of expression profiles are shown in Figure 26. The X-axis represents the°herbal concentration from low to high and the Y-axis is the gene-expression ratio~,=~Th~ aignature genes were selected from the expression profiles which exhibit dosage response~to the PHY906#2. The induced and repressed genes were selected from cluster 3 &°4 and cluster 18 &~ 19, respectively. In order to identify signature genes for PHY906, another:b~atcli~.of Huang Chin Tang, PHY906#3, with the same formula and manufacturing process were performed as described for PHY906#2. The induced and repressed genes commonly foundin both batches are shown in Figure 27.
Score similarity of bioresponses by self organizing map (SOlVn To differentiate.herbal 'medicines of similar compositions, a scoring method is developed and the score S represents the'difference in bioresponses of a biosystem to two different herbal compositions.
S = E P,~W~, '~
Where Pty is the number' of the common genes induced both by herbal prep. A
and herbal prep. B iri cluster i and in cluster j. For example, the SOM clustering results for the expression profiles of both batches of P ~.HY906'are shown in Figure 28A. In cluster C13 and C14, 17 and genes share the same 'eXpressiom profiles for both batches of PHY906, respectively. In addition, 10 genes whose'eXpres'siori profiles induced by PHY906#2 are clustered in C13 but are clustered in C14 for.PH~'906#3. Therefore, P~313=17, P1a14= 25, and PI314=lO. A weighing factor, Wl~, describes the d"istance'between the cluster i and j to indicate the similarity of the 20 two expression profile clusters. In the case of C13 and C14, these 10 genes have similarly response to PHY906#2 and PHY90;6#3 '(Figure 28B). The weighing factor is defined as:
Wl~ =1- EZ~ /Max(El~), where E~~ ~is the Euclidean distance between the cluster i and j and the value is normalized by Elf /MaX(E1~). When i = j, W=~ is 1. The number decreases as cluster i and cluster j become more~different (Figure 28C).
25 Classification of 5 batches of herbal medicines To test how well the valiowle riiethod performs in classifying 5 batches of similar herbal preparations, Jurkat 'I' °cells v~e~eweated with 5 batches of Huang Chin Tang (PHY01040; #16, .. ~°, , . . .
PHY010402; #17, .PHY0306~1;',#18, PHY03062; #19 and PHY02231; #20 obtained from Sun Ten Pharmaceutical Co.) liy 5 concentrations (1, 1/2.5, 1/5, 1/10, and 1120 of ICSO). The S=~
scores were calculated between pairs of herbal preparations in cluster analysis (Figure 29). The analysis programs, Kitsch Cluster was based on hierarchical clustering principle and was written by Dr. Joseph Fel~en'stein of ~7Vashington University (http://evolution.genetics:washington.edu/phylip.htrnl). The S scores (distance) are tabulated (Figure 29A), one can clearly identify.that the shortest distance lie between batch #17 and batch #18 and that batch#:17 is.similar to #18. Batch #16 also similar with batch #17 and #18 but batch #19 is dissimilar to the rest of batches. .The results were confirmed by HPLC
analyses.
Characterize an unknown herbal medicine based on the expression profiles To identify an unknown herbal medicine based on the characteristic expression profile database established as described asabove, Jurkat T cells were treated with a tester sample #17 in 5 concentrations to set up the characteristic expression data set for the tester. The S score between the tester and eacli~of the data sets (#16, #17, #18, #19 and #20) in the characteristic expression.database were calculated and the S scores are: #I6: 0.78, #17:
0.85, #18: 0.84, #19:
0.77, and #20: 0.79.These data show that the tester is most similar to #17 having a higher S
score of 0.84. The example deindiistrates that one can apply the method to identify an unl~own herbal medicine based~on the gene expression profiles induced by the herbal medicine in mammalian cells. The identity of the.unknown herbal medicine can be inferred by aligning the characteristic expression profiles with a collection of characteristic expression profiles of herbal~medicinesvin'an.HBR Array.
The property of an'herb can be described by four natures and five flavors (in Chinese Herbal Phramaceuticals; Ed. Zheng Hua Yen, People's Health publications, Beijing, China, 1997; Book of Ben Cao Gan IVIu by Shi Zeng Li, Ming Dynasty, China). Each of the four herbs in PHY906 may relate;to_another set of herbs with similar property (see Table 19). Or herbs with similar propeity~~rriay'exhibit similar bioresponse. HBR Arrays may be used to determine or measure the :relatedness in terms of the property of herbs. Such information may be useful in creating a new Herbal formulation.

7Cable 19: Herbs with PHY906 Properties Herb Properties Drugs with similar properties , -Scutellariae Bitter tasting,Coptidis Rhizoma, Phellodendri Radix . Cortex, Gentianae Radix, cold natured~Gardeniae Fructus Paeoniae RadixBitter and Canarii Fructus (sweet and sour sour ' ~ tasting, moderate tasting, natured), Potulacae Herba (sour mildly., tasting, cold natured), ' .

cold in nature:. Fraxini. Cortex( bitter tasting, cold natured), Sophorae ., Flos (bitter tasting, mild cold in nature), Bletillae Tuber (bitter, sweet and harsh in taste, and mildly cold in nature).

Glycyrrhizae Sweet tasting,Lycii Fructus, Polygonati Officinalis Rhizoma, Polygonati Radix moderate Rlaizoma natured Zizyphi FructusSweet tasting.Sacchartim Granorum, Juglandis . and' Semen warm'natured Properties:
Four natures-cold; hot; .warm, cool. ' Five flavors-acrid, bitter., sweet;sour, bland.
Example 18. Evaluation ~of an herbal medicines by HBR Array.
As stated in Example'16','the component herbs of Huang Chin Tang are Scute, Peony, Licorice and Jujube. .The geiie,expression profiles induced by five batches of Huang Chin Tang in mammalian cells were cliaracterized. A standard formula for Huang Chin Tang can be defined and characterized with animal studies or with clinical studies. For example, the #17 was used as the standard formula for. Huang Chin Tang based on the quality control and other standards set up by Sun.Ten'Pha'rniaceutical Co. The bioresponses of #17 were used to build the HBR Array for Huang~ Chin Tang. The marker genes in the HBR Array were selected to evaluate other preparations "of Huang Chiri Tang composition. A tester Huang Chin Tang may contain the same herb compositions but the component herbs may be grown under various environmental characteristics: Comparing the bioresponses of the tester with the marker genes of standardized HBR Array.~the biological activities of the tester were evaluated.
Furthermore, the inarker'genes whose expression levels are lv.ghly correlated (with ~R~>0.99) with the dosage of corriponent herbs in Huang Chin Tang (as stated in Example 16 and Figure 25) are selected, for' evaluation purpose. The tester Huang Chin Tang can be evaluated by comparirig'the specific bioresponses or expression levels of the selected set of marker genes with the HBR AiTay. 'Tf'the expression levels or bioresponses of tlae selected marker genes are~beyond'the~acceptable variation region, the amount or characteristics of the component herbs 'are adjusted or modified to meet the acceptable variation.
The process is repeated until the bioresponses induced by the revised herbal composition are within the acceptable variation range by comparing with the standard HBR Array.
Example 19. Pi-edicting~biological,- activit~and therapeutic applications of an herbal composition. ~ " ~ ~' According to the identified marker genes for PHY 906 (Figure 27), these genes can be used to predict the biological activities of the herbal composition. For example, the following underlined marker genes of PHY906 have been reported to involve in the following biological activities and therapeutic effects. ~ The only effective drug against ALL is to inhibit the aspara ine svnthetase due to increased cellular apoptosis (Handy et al., 1998). Long-acting drug somatostatin analogs are applied in the treatment of neurofibroma for their tumor growth inhibitory effect because they induce antiproliferative action mediated by the inhibition of 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). Mito,~en-activated protein kinase (1VIAPKI family members have been recently reported to have opposing effects on apoptosis (Dabrowski et al., 2000). The expressions of asparagine synthetase, transketolase,~ ephrim-A1'- and MAPK are repressed with the higher concentration of PHY906 treatments. 'The dovsin=regulation of these genes are involved in cell apoptosis. The expression of the enzyme argininosuccinate synthetase, cathepsin G and chemokirie RANTES
are highly induced in inflammatory mechanism. By the PHY906 treatment the inflammatory involved genes are suppressed: These literature reports provide a basis for predicting the biological activities or therapeutic.effects of an herbal composition.
References for Exaiiilile 19.
Handy-P; Periclou~AP; Avramis-VI (1998) The synergism of 6-mercapopurine plus cytosine arabinoside~follo~ued 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;~~'u'suf FI; Cascante-M; Williams-RD; Schirmer-WJ (1998) Inhibition of the oxidative and' nonoxidative pentose phosphate pathways by somatostatin: a possible mechanism of antiturilor 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=Al.during melanoma progression. Tnt. J. Cancer 84:494-501 Dabrowski-A; Tribilla-I; Dabrowska MI; Wereszczynska-SU; Gabryelewicz-A (2000) Activation of mitogen-activated protein kinases in different models of pancreatic acinar cell damage. Z-Gastroentero1..38: 469-4:81 The foregoing detailed description has been given for clearness of understanding only and no unnecessary limitations should be understood therefrom as modifications will be obvious to those skilled in the art. ~ : ~ ..
While the invention has been described in connection with specific embodiments thereof, it will be understood. thatf if is capable of further modifications and this application is intended to cover any variations,yu~es; ;or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or custorriary'practice:v,~ithin the art to which the invention pertains and as may be applied to the 'essential features hereinbefore set forth and as follows in the scope of the appended claims.

Claims (24)

WHAT IS CLAIMED IS:

1. A method of establishing a standardized Herbal BioResponse Array (HBR
Array) for an herbal composition comprising:

d) selecting a characterized herbal composition;

e) exposing a biosystem to a batch of the characterized herbal composition and collecting data on two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising:

iv) producing a cell banking system;

v) profiling the gene expression pattern of cells from the cell banking system before arid after exposure to the herbal composition;

vi) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

f) storing the marker data of step b) as a standardized HBR array.

2. The method of claim 1, further comprising:

g) repeating steps b) and c) for one more batches of the herbal composition using two or more of the same or different markers than used in step b);

h) combining the HBR Arrays obtained in steps c) and d); and i) analyzing the combined HBR Array of step e) to generate a standardized HBR
Array for the characterized herbal composition.

3. The method of claims 1 or 2, wherein the characterized herbal composition has at least one known BioResponse.

4. The method of claims 1 or 2, wherein one or more of the following is known for the characterized herbal composition: chemical testing, the part of the plant used, the growing conditions of one or more of the individual herbs in the characterized herbal composition, the pre-harvest treatment of one or more of the individual herbs in the characterized herbal composition, the post-harvest treatment of one or more of the individual herbs in the characterized herbal composition, the post-harvest treatment of the characterized herbal composition, and the relative proportions of the individual herbs in the herbal composition.

5. The method of claims 1 of 2; wherein the cell banking system comprises a master cell bank and a working cell bank a.

6. The method of claim 5, wherein the cells of the working cell bank are obtained from the master cell bank.

7. The method of claim 5, wherein the step of profiling the gene expression pattern of cells from the cell banking system before and after exposure to the herbal composition is performed using cells from the working cell bank.

8. The method of claims 1 or 2, wherein the change in gene expression is determined using a nucleic acid microarray.

9. The method of claim 8, wherein the said genes whose expression levels are changed by exposure to the herbal composition are selected based on the criteria of having a signal to noise ratio of about 2.5 or greater in the nucleic acid microarray and having an about 1.5 or greater change in the differential expression ratio.

10. The method of claim 8, wherein data regarding between about 10 and about 20,000 genes whose expression levels are changed is stored as part of the HBR Array.

11. The method of claim 10, wherein data between about 10 and about 1,500 genes whose expression levels are changed is stored as part of the HBR Array.

12. A method of evaluating an herbal composition comprising:

a) exposing a biosystem to a batch of the herbal composition and collecting data on two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising:

i) producing a cell banking system;

ii) profiling the gene expression pattern of cells from the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

b) comparing the collected marker data with a standardized HBR Array for the same or a substantially same herbal composition as that of the batch herbal composition, wherein the standardized HBR Array contains one of the markers data on gene expression.

13. A method of determining if an herbal composition meets a standard specification comprising:

a) exposing a biosystem to a batch of the herbal composition and collecting data on two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising:

i) producing a cell banking system;

ii) profiling the genie expression pattern of cells the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

b) comparing the collected marker data with a standardized HBR Array for the same or a substantially same herbal composition as that of the batch herbal composition, wherein the standardized HBR Array contains as one of the markers data on gene expression;
and c) determining which herbal compositions have marker data that is similar to that of the standardized HBR Array within an acceptable level.

14. The method of claim 113, wherein said determining which herbal compositions have marker data which is similar to that of the standardized HBR Array within an acceptable level is determined quantitatively or qualitatively.

15. The method of claims 13 or 14, wherein the standardized HBR Array includes an acceptable range of variation for each marker.

16. A method of adjusting the components of a herbal composition so that it meets a standard specification for the same or substantially the same herbal composition, comprising:

a) exposing a biosystem to a batch of the herbal composition and collecting data on two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising:

i) producing a cell banking system;

ii) profiling the; gene expression pattern of cells the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

b) comparing the collected marker data with a standardized HBR Array for the same or a substantially same herbal composition as that of the batch herbal composition, wherein the standardized HBR Array contains as one of markers data on gene expression, and wherein the standardized HBR Array also includes an acceptable range of variation for each marker;

c) determining whether the herbal composition has marker data that is within the acceptable level of variation for the standardized HBR Array; and d) if the marker data is not within the acceptable level of variation for the standardized HBR
Array, adjusting the components of the herbal composition.

17. The method of claim 16, wherein steps (a) through (d) are repeated until the marker data of the herbal composition is within the acceptable level of variation of the standardized HBR
Array.

18. A method of changing the components of a herbal composition so that it meets a standard specification of another herbal composition, comprising:

a) exposing a biosystem to a batch of the herbal composition and collecting data on two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising:

i) producing a cell banking system;

ii) profiling the gene expression pattern of cells the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

b) comparing the collected, marker data with a standardized HBR Array for the other herbal compositions as that of the batch herbal composition, wherein the standardized HBR Array contains as one or markers data on gene expression, and wherein the standardized HBR
Array also includes an acceptable range of variation for each marker;

c) determining whether the herbal composition has marker data that is within the acceptable level of variation for the standardized HBR Array; and d) if the marker data is not within the acceptable level of variation for the standardized HBR
Array, changing the components of the herbal composition.

19. The method of claim 18, wherein steps a) through d) are repeated until the marker data of the herbal composition is within the acceptable level of variation of the standardized HBR
Array.

20. A method for predicting the biological activity of an herbal composition comprising:

a) exposing a biosystem to a batch of the herbal composition and measuring the differential responses of two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising;

i) producing a cell banking system;

ii) profiling the gene expression pattern of cells the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

wherein the set of differential response measurements constitute an Herbal BioResponse Array (HBR Array) data set;

b) comparing the HBR Array of the batch herbal composition to at least one previously-obtained HBR Array of a characterized herbal composition, wherein the previously-obtained HBR Array contains as one of the markers data on gene expression; and c) predicting the biological activity of the batch herbal composition based on the HBR Array comparison made in step b).

21. A method of measuring the relatedness of a herbal composition to a characterized herbal composition comprising:

a) exposing a biosystem to a batch of the herbal composition and measuring the differential responses of two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising;

i) producing a cell banking system;

ii) profiling the gene expression pattern of cells the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

wherein the set of differential response measurements constitute an Herbal BioResponse Array (HBR Array) data set;

b) comparing the HBR Array of the batch herbal composition to at least one previously-obtained HBR Array of a characterized herbal composition, wherein the previously-obtained HBR Array contains of the markers data on gene expression; and c) determining the relatedness of the herbal composition to the characterized herbal composition based on the HBR Array comparison made in step b).

22. A method for predicting new therapeutic applications of an herbal comprising:

a) exposing a biosystem to a batch of the herbal composition and measuring the differential responses of two or more markers, wherein one of the markers is a change in gene expression determined through the use of a nucleic acid microarray, produced by the steps comprising;

i) producing a cell banking system;

ii) profiling the gene expression pattern of cells the cell banking system before and after exposure to the herbal composition;

iii) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

wherein the set of differential response measurements constitute an Herbal BioResponse Array (HBR Array) data set;

b) predicting the new therapeutic applications based on the predicted biological activity of the markers in the HBR Array.

23. A method for determining the gene expression profile induced by individual chemical entities in an herbal composition, comprising:

a) producing a cell banking system;

b) profiling the gene expression pattern of cells from the cell banking system before and after exposure to the herbal composition;

c) selecting as markers those genes. whose expression levels are changed by exposure to the herbal composition and placing into an HBR Array;

d) comparing the HBR Array generated in step (c) with a standardized HBR Array for a similar or modified herbal composition;

e) determining the relative amounts of the individual chemical entities of the herbal composition; and f) comparing the amount of the individual chemical entities to the result of step (b) to identify those genes whose expression levels change as the amount of the individual chemical entity in the herbal composition changes.

24. A method for determining the gene expression profile induced by individual chemical entities in a complex mixture without extracting the chemicals from the complex mixture such as an herbal composition, comprising a) producing a cell banking system;

b) profiling the gene expression pattern of cells from the cell banking system before and after exposure to the herbal composition;

c) selecting as markers those genes whose expression levels are changed by exposure to the herbal composition;

d) comparing the collected marker data with a standardized HBR Array for a substantially same or modified herbal composition;

e) characterizing the chemical components of the said herbal compositions;

f) comparing the identified chemical compositions to identify the differential levels of individual chemical components in herbal compositions;

g) correlating the differential chemical component amounts with the differential bioresponses in the HBR Array to identify the characteristic bioresponses for each chemical entity.