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New perspectives for synergy research with the "omic"-technologies.


Synergistic effects, understood as true overadditive effects, are often observed in experimental and clinical studies using phytopharmaceuticals. The introduction of the "omic"-technologies is now opening new perspectives in rationalizing these effects and making use of them in the development of a new generation of phytopharmaceuticals. This review describes possible mechanism of synergistic actions of herbal drugs by mono- and multitargeting and by the activation of signal cascades. It examins the possibilities of the standardization of single and multi component plant extracts and the prediction and assessment of the toxicity and safety of plant extracts with the support of the "omic"-technologies. It further discusses the use of phytopharmaceuticals in the context of an "individualized medicine". It makes proposals how to use the "omic"-technologies to rationalize and develop combination therapies of phytopharmaceuticals and synthetic drugs to minimize adverse reactions (ARs) or improve the therapeutic efficacy. Examples of clinical studies are given which explore already the potential of such co-medications. Modern medical therapy has acknowledged for quite some time the usefulness of combination therapies in the treatment of multifactorial diseases like cancer, cardiovascular or rheumatic diseases. The term "synergy (1)' is rarely used in this context, the combinatory mechanisms of actions seldom completely understood and the potentially occurring adverse reactions feared. A systematic exploitation of synergy effects of phytomedical interventions alone or in combination with synthetic drugs should lead in a long term perspective to the discovery and development of more rational evidence-based interventions in the prevention and therapy of multifactorial diseases and should thereby enrich modern pharmacotherapy.

[C] 2009 Elsevier GmbH. All rights reserved.

Keywords: Synergy; Omic-technology; Signal-cascades; Co-medication


Part I described the concept of synergy effects of phytopharmaceuticals based on a) empirical observations, b) in vitro- and in vivo pharmacological investigations and c) comparative clinical studies with synthetic monosubstances. Results suggest that the combination of many active components of plant extracts or plant extract combinations can lead to synergy effects (Wagner 2006). These synergy effects, understood not only as additive effects but as a true synergism according to Berenbaum (1989) may not only cause a better effectiveness with lower dosages of the single components, but should also lead to reductions of adverse reactions (AR). Modern medical therapy presently starts to acknowledge this synergy concept and uses also combination therapies in the treatment of several diseases like cancer, cardiovascular or rheumatic diseases--often without observing the originally expected cumulation of ARs of the single treatments. The term synergy is rarely used, but a systematic exploitation of synergy effects in drug development is likely to enrich modern pharmacotherapy.

The recent introduction of the "omic"-technologies (genomics, transcriptomics, proteomics and metabolomics) may allow us for the first time to analyse complex modes of action and may thereby increase the speed of our understanding of combination therapies and synergistic effects. "Omic"-technologies are today high throughput out techniques. Jointly applied in combination with bioinformatics, they are summarized under the heading "molecular system biology approach". This approach has entered more or less all areas of biological research. E.g. it is judged to provide unique insights into the molecular mechanisms leading to heart failure (Arab and Liu 2005), into processes like the instent-restenosis (Ganesh et al. 2004), into rheumatic diseases (Ferraccioli et al. 2004) or drug discovery and development (Katz et al. 2006). In the context of phytomedicine this approach is likely to open new perspectives for our understanding of the mode of action of complex mixtures and thereby may change our general attitude to phytomedicine. The following topics will elaborate this subject further:

(a) Synergism, mono- and multitargeting of drugs

(b) Standardization of single and multicomponent plant extracts

(c) Assessment and prediction of toxicity of plant extracts

(d) Improvement of preparations through new combinations of plant components involving traditional systems of medicine, thereby leading to the development of a new generation of standardized phytopharmaceuticals.

Synergism, mono- and multitargeting of drugs

The investigations of synergism understood as an agonistic drug action described by Berenbaum (1989) (E(a, b) > Ea + Eb) have so far examined the combinatorial effects of drugs at a single time point in one experimental setting. But the phenomenon of synergism of drug action in a human body or in any living organism is likely to be a dynamic process and a multitarget phenomenon. Imming et al. (2006) already described that it ultimately would be desirable to move away from a static to a dynamic target definition of drug action.

The developing "omic"-technologies provide us now with the possibility to detect the interaction of a drug with several targets and have indeed already demonstrated multitarget effects of single components. Table 1 shows a few examples e.g. the gene-expression profiling of [alpha]-tocopherol showed the targeting of genes related to the immune system, as well as the activation of genes related to the lipid metabolism and to inflammation interestingly with no significant change in the expression of classcial antioxidant genes (Vasu et al. 2007). Similarly methotrexate (MTX) or mecaptopurin targeted a multitude of genes involved in apoptosis, mismatch repair, cell cycle control and the stress response (Cheok et al. 2003). This demonstrates convincingly that both--single synthetic drugs or single natural substances can have a multitude of targets on the genetic level (Table 1). Simultaneous proteomic analysis demonstrates that at least part of the genomic regulation is translated into the functional level (Table 1). Thus, the "omic" technologies lead us away from the paradigm of "one drug, one target and one disease". This approach is clearly outdated.
Table 1. Monosubstance "Drugs" with multiple targets.

   Drug/         Gene targets (examples)    Protein targets (examples)

[alpha]-       Immune response, phosphate   Proteinkinase C,
Tocopherol     metabolism, protein          Phospholipase A2,
               modification, lipid          5-Lipoxygenase (review:
               metabolism, inflammatory     Ulrich-Merzenich et al.
               responses (Vasu et al.       2009)

Daidzein       Androgenresponsive genes,    Oestrogen-receptors,
               IGF-1 Pathway, MAP kinase    Peroxisome- Proliferator
               pathway genes (Takahashi et  -Activated Receptors (Dang
               al. 2006)                    and Lowik 2004)

Quercetin      Immune Response              20 proteins (e.g. heat
               (Interleukines like IL-1,    shock proteins, annexins
               IL-6 or cytokines like       involved in apoptosis,
               TNF-[alpha])                 metabolism, detoxification
               (Ulrich-Merzenich et al.     and gene regulation (Wenzel
               2008)                        et al. 2004)

Methotrexate   S 100 calcium binding        Dihydrofolic acid, T-Cells,
               protein A8 and A9;           Livertransaminases (Folic
               Leucocyte immunoglobulin     acid synthesis inhibition)
               receptor; defensin, alpha
               -3, neutrophil specific
               (Cheok et al. 2003)

Mercaptopurin  Splicing factor proline/     Adenylosuccinate-synthase,
               glutamine rich; nucleoside   Phosphoribosylpyrophosphat
               diphosphatase kinase type    -amidotransferase, (DNA-and
               6, CDC28 protein kinase 2,   RNA-synthesis-inhibition)
               polymerase (DNA directed),
               gamma 2 (Cheok et al. 2003)

Single natural substances or synthetic drugs can act on a multitude
of targets on the genetic level. Parts of the gene regulations are
translated on to the protein/functional level.

If monosubstances have not only one, but multiple targets, a group of drugs or multicomponent mixtures are expected to have multiples of these targets. This multiplicity of targets and the expected subsequent unpredictable mode of action has over many years been the main argument against the use of combination therapies or against fostering the use of phytopreparations. It was expected that the adverse reactions (AR) of single drugs would equally multiply in a combination therapy. However, the potential of synergistic positive effects was not really considered.

With respect to the multiplicity of targets, we have seen in our own studies that the number of active components of a plant extract does not necessarily determine the number of targets. In a fibroblast model we compared the modulation of genes by acetylsalicyclic acid (ASA) and by the multiextract mixture Phytodolor[R] (PD) as well as its single extract components using the topic defined PIQUOR[TM] Skinpatho Microarray which measures the modulation of inflammatory genes. The multiextract mixture PD is composed of three alcoholic extracts: Populus tremula (S1), Solidago virgaurea (S2) and Fraxinus excelsior (S3). S1 possesses a high content of salicin, salicylates (1) and salicyl alcohols, but also phenolic components like flavonoids and catechins (Nahrstedt et al. 2007). S2 has a high content of rutin and S3 a high content of fraxin. Each of the single extracts modulated a different number of genes based on the microarray assessment: S1 modulated 51 genes, S2 24 genes and S3 31 genes. The extract combination PD did not reveal an additive modulation of genes, it modulated "only" 40 genes and ASA 44 different genes. Thus, the number of active components in an extract does not necessarily determine the number of targets (Ulrich-Merzenich et al. 2007c).

We observed further that the gene expression profiles of the single extracts S1, S2 and S3 did not allow a prediction of the gene expression profiles of their combination (S1+S2 + S3) (Ulrich-Merzenich et al. 2007c). A comparable observation was made by Cheok et al. (2003) earlier. Gene microarrays (HG-U95A oligonucleotide microarray) were applied to investigate the mode of action of methotrexate (MTX) and mercaptopurin alone and in combination on human leukemia cells. It was demonstrated that the combination of both causes in human leukemia cells a different gene expression profile than each single applied drug. The expression profile of the combination shared only 14% of genes with the ones of the single application. Thus, 86% of the previous modulated genes did not respond (Cheok et al. 2003). This demonstrates that drug combinations can lead to the activation of more or less entirely different sets of genes compared to those activated by the single agents (Ulrich-Merzenich et al. 2007b). Thus, the application of drug combinations including phytopharmaceuticals does not necessarily mean the addition or multiplication of targets. It can lead to new modes of actions. The consequences of these findings specifically for phytopharmaceuticals are: the mode of action of a medication is most reliably assessed if all applied single drugs are taken into consideration since a new mode of action may arise through the combination of drugs. Therefore, the demand to demonstrate the mode of action of each single component in a phytopharmaceutical may not be obligatory any more.

Do all multifactorial diseases require a multitarget approach in the form of combination therapies?

Combination therapies are today daily practice in many areas like cardiology, oncology or rheumatology. The use of combinations of potentially valuable drugs rather than their sequential use has indeed been proposed already in the 90s for rheumatoid arthritis (RA). The argumentation was that the chronicity of RA reflects the failure to suppress multiple parallel pathologic pathways and therefore initial "broad spectrum" coverage might be a reasonable therapeutic approach (Klippel 1990). The goals of combination therapy were formulated as an improved (clinical) efficiacy, lower doses of drugs, and less drug toxicity (Klippel 1990). But in the 90s this approach was discussed controversially. The superiority of the use of combinations of second-line antirheumatic drugs over the single-agent treatment could not be demonstrated convincingly. In some trials the use of combinations of synthetic drugs led to an increase of ARs (review CANNON and Ward 1993). The introduction of the new biotechnology-derived treatments with "biologicals" (antibodies towards cytokines or subpopulation of immunecompentent cells) changed the scenario. E.g. in the treatment of RA, one of the most common and potentially severe rheumatic diseases, the combination of methotrexate (MTX) and TNF[alpha]-inhibitors (biological) is superior to the application of the TNF[alpha]-inhibitors or MTX alone with respect to clinical, radiographic, and functional benefits and notably without increasing ARs (Pincus et al. 2003, St. Clair et al. 2004). Meanwhile, not only TNF[alpha]-inhibitors, but also many other biologicals are applied in combination with MTX because this strongly increases the magnitude and duration of the therapeutic response (Fiehn 2009). However, MTX has side effects which are attempted to be overcome. In this context the scope of co-medications with phytopharmaceuticals with immunomodulatory, hepatoprotective or antiinflammatory properties could be considered.

Part I already described the work of Hemaiswarya et al. (2008), who showed that the combination of antibiotics with phytopharmaceuticals can enhance and improve the action of antibiotics. We earlier described clinical studies in which synergistic effects of single herbal drugs or their combination were observed (Ulrich-Merzenich et al. 2007a-c) and we gave examples of studies in which the therapeutic equivalence of phytopharmaceuticals with synthetic drugs was seen (Wagner and Ulrich-Merzenich 2009). Table 2 shows a selection of clinical studies in which the scope of the combination of conventional therapies and phytopharmaceuticals has been explored.
Table 2. Comedication studies of herbal and synthetic drugs and
conventional therapies.

       Indication             Herbs/major       Conventional synthetic
                             constituents          drugs/treatments

Oncology Breast-,        Viscum album L. (sME   Chemotherapy
Ovarian, non-small Lung  Helixor[R]) or
Cancer (Schierholz el    Lentinan as Control)
al. 2003)

Mammary Carcinoma UICC   Visucum album L.       Chemo-, Radio-,
Stage I-III (Bock et     (Iscador[R])           Hormontherapy
al. 2004)

Primary malignant        Viscum album L.        Chemo-, Radio-,
Melanom, UICC-/AJCC      (Iscador[R], FME)      Immunotherapy
stage II-III (Augustin
et al. 2005)

Nasopharyngeal           "Destagation" Chinese  Radiotherapy study
Carcinoma (Xu et al.     herbs

Pain Healthy volunteers  Vitamin C              Acetyl salicyclic acid
(Konlurek et al. 2004)

Healthy volunteers       Vitamin C              Acetyl salicyclic acid
(Dammann et al. 2004)

Pain (Chrubasik 2000)    Urticae herba          Non steroidal
                                                antiinflammatory NSAID

Arthritis (Chrubasik et  Urlicae Herba fresh    Diclofenac
al. 1997)                plant

Hyperlipo-protcinacmic   Psyllii semen          Diet,
(Brock etal., 2001)      Kneipp[R] Granulal     cholesterin-reducing
                         Psyllium               drugs

(Spence et al. 1995)     Plantaginis ovatae     Colestipol
                         semen (POS)

Organ transplantation    Hypericum perforatum   Cyclosporinc
(Ernst 2002)

Skin erythema            Hypericum perforatum   UVB, UVA, visible
Pigmentation Skin type   (LI 160)               light, solar
II,III (Schempp el al.                          simulated radiation

Healthy volunteers       Hypericum perforatum   Low dose oral
(Will-Shahab et al.      (Ze 117) low dose      contraceptives
2009)                    Hyperforin             (Ethinylestradiol +

Healthy volunteers       Hypericum perforatum   Midazolan (n = 20)
(Mueller et al. 2009)    Low dose hyperforin

Graves' Disease          Ginkgo biloba extract  Radioiodine Therapy
(Dardano et al. 2007)    (Egb 761)

      Indication             Study design              Effects

Oncology Breast-,        Multicentric,          Quality of life [up
Ovarian, non-small Lung  randomised open,       arrow] (p <0.05) ARs
Cancer (Schierholz el    prospective study      less frequent
al. 2003)                (n = 233)

Mammary Carcinoma UICC   Multicentric,          Safe, fewer ARs of
Stage I-III (Bock et     comparative Cohort     conventional therapy
al. 2004)                study (n = 1442)       (p <0.001) suggested
                                                prolonged overall
                                                survival (p = 0.038)

Primary malignant        Multicentric,          Safe, prolonged
Melanom, UICC-/ AJCC     comparative Cohort     tumordependent survival
stage II-III (Augustin   study (n = 686)        (p = 0.002)
et al. 2005)

Nasopharyngeal           Randomised,            5-years survival rale
Carcinoma (Xu et al.     prospective (n =       [up arrow]- for the
1989)                    188)                   combination

Pain Healthy volunteers  Pilot study (n = 10)   ASS + Vitamin C Gastric
(Konlurek et al. 2004)                          lesion index [down
                                                arrow] protection of
                                                gastric mucosa

Healthy volunteers       Randomised four-fold   ASS + Vitamin C fewer
(Dammann et al. 2004)    crossover study        gastric lesions fewer
                         (n = 17)               microbleedings

Pain (Chrubasik 2000)    Observational study    Safe, reduction (64%)
                         (n = 8955)             or dis-continuation
                                                (26.2 %) of drugs

Arthritis (Chrubasik et  Pilot study (n = 37)   Comparable results in
al. 1997)                200 mg Diclofenac vs   reduction of joint
                         50 mg Diclofenac + 50  score + CRP-levels
                         g fresh plant

Hyperlipo-protcinacmic   Placebo controlled     Total Cholesterol [down
(Brock etal., 2001)      double blind (n =      arrow] 15 %
                         161)                   LDL-Cholesterol [down
                                                arrow] 16% independent
                                                of comedication

(Spence et al. 1995)     Controlled study (n =  No difference, but
                         121) 5.0g Colestipol   better tolerability
                         vs. 2.5 g Colestipol
                         + 2.5 g POS

Organ transplantation    Case studies, case     Cyclosporine level
(Ernst 2002)             series                 [down arrow].

Skin erythema            Prospective            No phototoxic potential
Pigmentation Skin type   randomised study (n =  up to a dose of 1800
II,III (Schempp el al.   72)                    mg/d

Healthy volunteers       Self-controlled (n =   No interaction of
(Will-Shahab et al.      16)                    hormonal components
2009)                                           with Ze 117

Healthy volunteers       Self-controlled        No clinically relevant
(Mueller et al. 2009)                           induction of CYP3A

Graves' Disease          Placebo controlled (n  Neutralization of
(Dardano et al. 2007)    = 25)                  genotoxic damage
                                                induced by radio-iodine
                                                treatment without
                                                affecting the clinical

Table 2 shows the results of clinical studies investigating the
potential and safety of combining phytopharmaceuticals with synthetic
drug treatments or radiotherapy. In some, but not all, efficacy and
adverse events are improved by the addition of herbal drugs. AR:
adverse reactions.

Data propose that the combination of conventional anticancer treatments with Viscum album extracts can reduce adverse reactions (ARs) or that the combination of acetyl salicyclic acid (ASA) with vitamin C protects from gastric lesions. The combination of the latter leads to an overadditive, synergistic effect on the expression of the heme- oxygenase-1 and on the bilirubin formation in the mucosal cells of the stomach. Antioxidative and protective effects on the cells of the gastric mucosa have been described for bilirubin already earlier (Grober 2007; Becker et al. 2003).

However, not all combinations of phytopharmaceuticals and synthetic drugs are of advantage as seen in the simultaneous application of Hypericum perforatum extracts and cyclosporin (Table 2). There is a strong evidence that Hypericum perforatum extracts lowers drug plasma levels of various anti-cancer agents by pregnane X receptor activation which leads to the induction of cytochrome P450 isotype 3 A4, P glycoprotein and several other enzymes (Kober et al. 2008). Other studies with Hypericum perforatum propose that this activation is primarily related to the applied dosage (Table 2). According to a recent meta-analysis on major depressive disorders (MDD), the treatment with Hypericum perforatum preparations did not differ from selective serotonin reuptake inhibitors in efficacy and adverse reactions. On the contrary, lower withdrawals due to ARs were judged to be an advantage of Hypericum perforatum preparations in the management of MDD (Rahimi et al. 2009). The recent Cochrane review (Linde et al. 2008) came to a comparable conclusion. The therapeutic equivalence of Hypericum perforatum extracts with synthetic mono-component drugs can only be explained with synergistic effects of the different constituents of Hypericum perforatum which each alone do not have this therapeutic potency.

The Mayo Clinic (Sood et al. 2008) published recently a survey on the use of phytopharmaceuticals or dietary supplements in combination with prescribed conventional medicine. 1795 patients participated in the survey of which 710 (39.6%) reported the use of dietary supplements or phytopharmaceuticals. The survey documented potential harmful interactions for the patients through the concommitant use of both. In total, 107 interactions were identified. The five most common natural products (garlic, valerian, kava, ginkgo, and St. John's wort) accounted for 68% of the potential clinically significant interactions. The four most common classes of prescribed medications with a potential for interaction were antithrombotic medications, sedatives, antidepressant agents, and diabetic agents. However, no patient was harmed seriously from any interaction and the actual potential for harm was judged to be low (Sood et al. 2008).

The potential of phytopharmaceuticals as co-medications to reduce ARs or even enhance efficacy has not yet been systematically explored. The first step would be the identification of diseases, especially multifactorial ones, in which a co-medication of synthetic drugs and phytopharmaceuticals could be successful. Sagar et al. (2006a) proposed herbs traditionally used for anticancer treatment to be developed as adjuvant cancer therapies in combination with chemotherapy and radiation. Proposed were Artemesia annua (Chinese wormwood), Viscum album (European mistletoe), Curcuma longa (turmeric), Camellia sinensis (green tea), Vitis vinifera (grape seed extract), Angelica sinensis (dong quai), Taxus brevifolia (Pacific yew), Scutellaria baicalensis (Chinese skullcap) Polygonum cuspidatum (Japanese knotweed), Silybum marianum (milk thistle), Magnolia officinalis (Chinese Manolia Tree), Ginkgo biloba and others (Sagar et al. 2006a, b). These herbs have in common that they show antiangiogenic activities in vitro via different mechanisms like inhibiting vascular endothelial growth factor, epidermal growth factors or targeting the inflammatory pathways COX-2 and NF-[kappa]B, or the Bcl-2 Protein (Review: Sagar et al. 2006a, b). It is suggested that their future use probably lies in synergistic combinations (Sagar et al. 2006a).

The "omic"-technologies may offer an adequate tool for the assessment of the resulting complex mode of actions of such combination therapies including the elucidation of synergistic effects.

The complexity of the "omic"-approach: a great challenge

An integrated approach using genomics, transcrip-tomics, proteomics and metabolomics for the assessment of the mode of action of multidrug treatments is likely to yield the most reliable results, but remains the great challenge for the future. The following aspects are of special interest to phytopharmaceuticals:

1. Standardization of single and multicomponent plant extracts

Prerequisite for the registration and the use of phytopharmaceuticals is their standardization. This is especially challenging for multiextract mixtures. So far standardization was based on the quantification of one or few identified biologically active substances and subsequent functional tests on the mode of action. The "omic" technologies allow us now to look into the standardization on a multitude of components. Expression signatures (bar codes) of plants or plant extracts are already used for the correct identification of medicinal plants (Sucher and Carles 2008) and can be used for standardization as well. Simultaneously the complex mode of pharmacological actions can be assessed. Ideally both signatures should be matched.

But first of all, the use of the "omic"-technologies should yield reproducible and specific expression profiles (signatures) for plant extracts. Fig. 1 shows the gene expression profile of the willow bark extract STW-33-1 in human chondrocyte cultures. In four different microarrays (Agilent human whole genome microarray), a specific profile of the willow bark extract in comparison to other tested substances could be recognized. In the given example four arrays with two different concentrations of the willow bark extract were investigated. For standardization, however, the use of more than four microarrays in different concentration would be more reliable to obtain the specific and reproducible core set of genes, which is activated by a plant extract. Line nr. 7 and 8 show the gene expression profile of the reference substances diclofenac and acetylsalicyclic acid (ASA). The comparison of expression profiles of complex mixtures with known reference substances (mono-substances) is useful, since this will simplify the identification of the essential pathways which finally lead to the observed clinical or pharmacological effects. Fig. 1 shows the hierarchical clustering of genes which are simultaneously up- or down regulated. Thereby common pathways are identified. In order to cope with the flood of bioinformatic data, it will be advisable not to start measurements with too complex combinations of extract mixtures.


The finding that reproducible gene expression profiles can be obtained in cell culture models for plant extracts is supported by recent findings of Pakalapati et al. (2009). Rats were treated with a Trifolium pratense extract rich in isoflavones. Whole genome microarray (Affymetrix Rae 230_2) measurements showed a reproducibly modulated core set of genes. The microarray data were combined with proteom studies. Those revealed that the modulation of several genes of the lipid metabolism was converted onto the protein level.

The reproducibility of metabolomic expression profiles induced by multicomponent mixtures is already supported by several studies in the field of nutrigenomics, e.g. Wang et al. (2005) demonstrated with an 1H NMR spectroscopy-based study in conjunction with chemometric methods differences between the metabolome pattern induced by chamomile tea in urine before and after tea ingestions. The effect of a dietary intervention of soy isoflavones in humans was equally detectable by NMR (Solanky et al. 2003; Rochfort 2005).

For a complete understanding of the mode of action, the integration and correlation of data sets from the different "omic"-areas and from functional tests is essential, but on the other hand tremendously challenging. E.g. not all expressed mRNAs are converted into proteins. Also the differential timings of these events is a challenging factor. Bilello (2005) proposed towards the understanding of pathophysiological processes an integrated system based approach involving modelling and simulating the complex dynamic interactions between genes, transcripts, proteins, metabolites and cells, encompassing many of the "omic"-technologies and using computational and mathematical models to analyse and simulate networks and pathways. This could also take into account spatial and temporal relationships that give rise to cause and effect in biological systems (Bilello 2005). Data of such integrated approaches have so far not been published in the field of phytomedicine. However, an integrated approach based on "omic"-technologies was already used e.g. by Kleno et al. (2004) who identified potential biomarkers (related to the glucose and lipid metabolism and to oxidative stress) for hepatotoxicity in rats. Rochfort (2005) showed that correlations from liver mRNA, liver proteome, and metabolome analysis of serum corresponded to changes in glucose, lipid metabolism and oxidative stress responses. Mayr et al. (2004) combined proteomic and metabolomic studies in the cardiovascular system and judged their results as follows: "The simultaneous assessment of protein and metabolite changes translated purely descriptive proteomic and metabolomic profiles into a functional context and provided important insights into pathophysiological mechanisms that would not have been obtained by other techniques."

For a standardization of plant extracts, the primary focus will be the reproducibility of profiles with the different "omic"-techniques resulting most likely in a tremendously improved insight into the pharmacological mode of action. Issues like the permitted magnitude of variation for each technology, however, still need to be defined. An overview about the currently available biological databases for annotations of genes and proteins such as GenBank, UCSC Genome Browser, Esembl. and non-sequence centric databases like the Protein Data Bank (PDB) has been given by Baxevanis (2006).

2. Pharmacokinetics and bioavailability of plant extracts and their combinations

Pharmacokinetic and bioavailability studies are an essential need to determine the exact pharmacological action of phytopharmaceuticals, but still unsufficient data do exist. Due to the high number of components in herbal drugs, their variable absorption and their complex biotransformation, assessments with complete coverage have been practically impossible to achieve by conventional methods. The new high-through put technologies facilitate these kinds of assessments and will improve the speed and yield. But again, even after the identification of the available plant components and their metabolites in plasma, functional studies are essential for determining the mode of action. This includes toxicology testing - the latter being the best developed field in the context of "omic"-technologies so far.

3. Prediction and assesment of the toxicity and safety of plant extracts

Each phytopharmaceutical needs to be assessed for safety and toxicity. Searfoss et al. identified the following general goals for the new field of Toxicogenomics equally applicable to the development of synthetic drugs and phytopharmaceuticals: a) understand mechanisms of toxicity 2) predict toxicity 3) develop in vivo and in vitro surrogate models and screens, and 4) develop toxicity biomarkers. These should lead to an improvement of safety, to the shortening of the drug development and a cost reduction. Reviews on the issue have been published by Searfoss et al. (2005), Suter et al. (2004) and Storck et al. (2002).

It is generally expected that the use of gene expression data is more sensitive than traditional toxicological endpoints (Searfoss et al. 2005). There is already a collaborative effort between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute and the National Institute of Environments Health Sciences National Centre for Toxigenomics Chemical Effects in Biological Systems knowledge base to establish a public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics data-bases. An overview of the major toxicogenomics database efforts in the public sector has been given by Mattes et al. (2004). These data bases are under development and offer on one hand information (gene expression profiles) on the toxicity endpoints dependent on organs as well as on different diseases. E.g. it has been estimated that the liver has 10-20 distinct toxic phenotypes, including steatosis, multifocal necrosis, hypertrophy, etc. (Searfoss et al. 2005; Zimmerman 2000). On the other hand they accept submissions from investigators to create and increase these data bases.

The Chemical Effects in Biological Systems (CEBS) knowledge data base ( is under development. Specific objectives have been formulated by Mattes et al. (2004) as follows: a) to compare toxicogenomic effects of chemicals/stressors across species - yielding signatures of altered molecular expression; b) to phenotypically anchor these changes with conventional toxicology data - classifying biological effects as well as disease phenotypes; and c) to delineate global changes as adaptive, pharmacologic or toxic outcomes - defining early biomarkers, the sequence of key events and mechanism of toxicant actions. CEBS is a dynamic concept for integrating large volumes of transcriptomic, proteomic, metabonomic, and toxicological knowledge in a framework that serves as a continually changing "heuristic" engine (Mattes et al. 2004). For further information and internet address lists see Mattes et al. (2004).

Nevertheless, some predictive toxicology products have already emerged by different biotechnology firms (Table 3). The development of these micro-arrays is based on the above mentioned type of investigations of gene expression signatures or "fingerprints" which are highly predictive for toxicity endpoints (phenotypes) (Searfoss et al. 2004) by testing toxic (test-) compounds. These products can be readily used for phytopharmaceuticals to screen for so called off- and on-target toxicities early in the drug developement. Off-targettoxicities are those caused by actions unrelated to the targets such as non-specific immune or inflammatory reactions or hepatotoxicities due to drug metabolism effects (Searfoss et al. 2004). On-target toxicities are additional unintendent effects due to the interaction of target and drug. Different tissues or cells can be exposed to the selected plant extracts and then screened for mRNA or metabolite modulation relevant for toxicity. Besides these already existing products (Table 3) it will be necessary to develop additional gene- or proteinchips specifically suited to the toxicology demands of phytopharmaceuticals.
Table 3. Readily available toxicology screening arrays are listed.

        Provider            Product                Description of

Iconix.Pharmaceuticals  DrugMatrix[TM]         Global expression     Chemogenomics          profiling in vivo + in
                                               vitro > 600
                                               compounds chemogenomic
                                               database, +200
                                               validated gene
                                               signature sets

Gene Logic              ToxExpress[TM]         ToxExpress[TM]                              reference data base
                                               predictive system with
                                               In vivo + in vitro
                                               models. Extensive
                                               liver toxicity

Exonhit                 Sale-Hit[TM]           DATAS[TM] screening                                technology reveals the
                                               presence of
                                               alternatively spliced
                                               mRNAs, and thus
                                               protein isoforms
                                               linked with toxicity

Miltenyi Biotec         Piquor Tox Microarray  Determination of                         classical toxic
                                               endpoint genes
                                               relating to apoptosis,
                                               DNA-damage + repair,

Affymetrix              GeneChip[R] Rat Tox    850 mRNA transcripts      U34                    and EST clusters
                                               selected from UNIGene
                                               database (Build 34)

Agilent                 Agilent 7890A/NPD/     Forensic Toxicology    5975C/DRS/GC/MSD       Data Base of 277

According to Searfoss et al. (2005), modified and data added.

4. Individualized medicine

The potential to screen genes and proteins involved in drug metabolism to reduce adverse drug reactions (ARs) is regarded the primary benefit of phamacogenetics/pharmacogenomics (Ferraccioli et al. 2004). Indeed, tremendous progress has been made in recent years to identify persons with different drug metabolizing capacities (e.g. polymorphisms in the cytochrom P450 enzyme activities). Thus, tailor made prescriptions for each individual patient after diagnosis is the new objective in pharmacology. Accepting the possibility of an individualized medicine needs a rethinking of proofs and requirements for drug development. Microarrays and other high-density/high throughput devices will play an increasing role in this area and will support the development of more personalized treatment regimes that accommodate individual genetic factors (Stafford 2007). Aim is the tailoring of drugs according to the "responsive" or "unresponsive" genotype of the patient and according to the patients drug metabolizing genotype and capacity. The need for such a selection has been especially recognized in rheumatology e.g. in the use of biologicals. These are high cost treatments involving possible ARs, which are generally very serious since biologicals target life-specific molecules (Ferraccioli et al. 2004). The identification of the most suited patient for those treatments would be highly useful to obtain the best response. Such an identification would be equally desirable for the treatment with phytopharmaceuticals. The individually differing induction of cytochrom P450 isoforms by Hypericum perforatum was already mentioned, the different potential to show allergic reactions is an equally important issue.

Interestingly all major traditional medical systems, the European tradition, the Ayurvedic and the Chinese medical systems are based on an individualized treatment concept with e.g. detailed descriptions of drug interactions, toxic actions according to time of application or combinations with food. These data and records from ancient systems of medicine in combination with clinical effects have so far not been explored due to a lack of methodology to cover the complexity of interactions.

Combining traditional knowledge of individualized treatments with the use of modern "omic"-technologies could boost the speed of the identification of patients for the different treatments to avoid ARs and generate the maximum benefit for the patient.

5. Synergism, signal cascades and trigger points of the metabolism

We discussed already that synergism can occur through multitargeting. But the identification of multiple targets, which will be simplified through the use of "omic"-techniques, will be only one part of the puzzle to understand synergistic actions. The type of target, which can be as diverse as enzymes, receptors, antibodies or signal cascades is equally important. Signal cascades are highly interesting targets for drug development since their activation can lead to an amplification of the original signal by a million times. The investigation of such signal cascades may elucidate our understanding of synergistic, but also antagonistic effects.

Recently G-protein-coupled receptors (GPCRs) have gained increasing importance as targets for drug development (Heilker et al. 2009). GPCRs constitute a large family of receptors which initiate various signal cascades. Such cascades may be called trigger points of the metabolism, not only because they amplify a signal, but they often can divert the signal into opposing directions. One example of such a trigger point is the mitogen activated protein kinase (MAPkinase) cascade. Several molecules like [H.sub.2][O.sub.2], vitamin C or the cytokine TNF-[alpha] (Karin 2005; Ulrich-Merzenich 2007a) can activate this cascade. The result of activation can be twofold- apoptosis or proliferation. E.g. the stimulation with a low dose of [H.sub.2][O.sub.2] over a short time leads to the activation of the MAPkinases ERK-1 and 2 and leads to proliferation. Contrary, a long time exposure of [H.sub.2][O.sub.2] in low dosage leads to apoptosis. A high dosage given shortly or applied for a long time leads also to apoptosis (Ulrich-Merzenich et al. 2009). Thus, the activation of the same signal cascade can lead to opposite effects depending on time and dosage of the trigger. Higher dosages of [H.sub.2][O.sub2] activate besides ERK-1 and 2 also the so called "stress induced" MAPkinases p38 and c-jun. Their additional activation leads to apoptosis instead of proliferation (Ulrich-Merzenich et al. 2007a-c).

Such dosage dependent reversal effects have been observed for several herbal drugs like Ginkgo biloba (Chan and Hsuuw 2007), Hypericum perforatum (Franklin et al. 2006) or Curcuma longa (Ali and Rattan 2006). One possible explanation for reversal, but also synergistic effects, could be such an activation of signal cascades. In case of reversal effects, the signal cascades antogonize each other in certain dosage ranges; in case of synergism signalcascades interact and agonize each other.

Indeed, the ultimate result of the activation of a signal cascade can be due to signal amplification much greater than originally calculated by the summation of the single effects. Imming et al. (2006) called these effects vaguely "rippels" in drug action. The "omic"-technologies can support the analysis of the simultaneous up- or down regulation of signal cascade molecules and thereby may factually describe these "rippels". Gene microarray-analysis identify via hierarchical clustering such genes which are commonly up- or down regulated (Fig. 2). Thereby pathways are identified. Synergism may be the result of the activation of central trigger points of such pathways and may be an expression of a non linear correlation of molecular interactions (of such pathways) and clinical effects. Such trigger points could be considered as new drug target or targets to be protected to safeguard a normal metabolism. As already mentioned the GPCRs have gained recently an extremely high interest as drug targets (Heilker et al. 2009). Several plant constituents like genistein (Si, Liu 2007) or flavonoids (Spencer 2008) like naringin and cyanidin-3-glucosides (Dallas et al. 2008) were already shown to interact with these. The potential of curcumin to modulate signalling pathways leading to cell cycle regulation or directly altering cell cycle regulatory molecules in cancer therapy is presently under intensive investigation (Sa and Das 2008). Synergism as the result of the coordinated activation of signal cascades may be a highly rewarding area for drug development. Synergism as result of an activation of transport proteins to increase the bioavailability of certain drugs will be described in a future review.


6. Improvement of preparations through new combinations of plant components involving traditional systems of medicine and thereby constituting the development of a new generation of standardized phytopharmaceuticals

Traditional systems of medicine like the Chinese or Ayurvedic systems have detailed description of methods to prepare their medicines. Even though tremendous efforts have been made e.g. in India over the past 40 years supported by the Council of Research in Ayurveda, Siddha and Unani to standardize these methods and at the same time to justify the different procedures, both goals are difficult to achieve due to the often high number of plants in a single medication and due to the high number of steps involved in the preparation. As already mentioned the "omic"-technologies allow the assessment of complex mixtures and could be used to monitor the different steps of the preparation in order to validate or dismiss the original recipes and to look into the mode of action of Ayurvedic preparations which contain sometimes up to 70 different plants (Ayurvedic Formulary of India 1978).

Chinese medicines are primarily prepared as watery decoctions. It is recognized since long times that this type of preparation is difficult to standardize. The Chinese Pharmacopeia is based on the qualitative and quantitaves analyses of alcohol extracts. The results, however, may not mirror the contents of the decotions. Fig. 2 shows that the application of different preparations of plant extracts lead in rats to clearly distinguishable patterns of gene expressions in blood cells. These differences are likely to be translated onto the protein and functional level, too. Thus, "omic"-technologies can be utilized to support the selection and the development of the optimal extract form or preparation for Chinese or Ayurvedic medications in an acceptable time frame. However, the following premises and recommendations may be considered to obtain reliable evidence-based "new" phytopharmaceuticals.

1. The basic material should be quality assured in form of a standardization according to conventional methods (fingerprint and if available fixed content of biological active substances in a combined effort).

2. A toxicology screening of the "phytopharmaceutical" should be performed with one of the presently already available screening tools (Table 3). In this context specific toxicology assays and data banks for critical plant components need to be developed.

3. An evaluation of the possibility to identify "surrogate" plant components which represent the activity of the plant extract should follow and could be used for standardization.

4. Standardization of the plant extracts according to an expression profile taking into consideration relevant concentrations and the bioavailability. Matching of plant signatures with signatures of the mode of action in different surrogate models should be attempted.

5. The determination of the down-stream effects influenced by phytopharmaceuticals and bioactive plant components via identification of molecular pathways including the identification of relevant mechanisms of action for the different plant components through comparison with reference-listed drugs

6. Integration of the profiles of single plant components and complex extract mixtures into the international freely accessible data banks of genetic, proteomic and metabolomic screens.

7. Exploration of connectivity of biological pathways by a systematic combination screening and thereby determining the mode of action (antagonistic, additive, agonistic = synergistic) of single components and extract mixtures in comparison to reference-listed drugs. Complex mode of actions of combination therapies (Table 2) should be investigated.

8. Based on plant and disease profiles, relevant clinical applications for phytopharmaceuticals should be identified.

Outlook and conclusions

"Omic" technologies are changing our conceptual framework of drug action and our understanding of the pathology of diseases. They allow the more or less full discovery of the multiplicity of drug targets and proof the one disease-one drug paradigm as outdated. Simultaneously, more personalized treatments and tailor made medicines are the new hope and scope for the future. Nevertheless, presently we are (only) far advanced with the data collection, but still without a thorough conceptual framework for the integration of the complexity of these data. Methodological challenges include the establishment of the reproducibility of results obtained by different methodological standards, the target selection, its validation and the scale of precision. The "omic"-technologies are an extremely promising tool to understand the mode of action of complex mixtures with their different targets and feed back loops. However, the challenge of handling plant extracts with varying concentrations of ingredients according to season or growth conditions will remain. The understanding of expression profiles created by herbal drugs on the genetic, proteome and metabolom level is likely to support a definition of concentration ranges required to yield the desired mode of action.

The most successful future path will involve a multidisciplinary integration of "standard" techniques like the standardization of the basic plant material by conventional methods with the screening of genetic model systems in combination with clinical studies after obtaining promising pre-clinical results. These efforts in phytomedicine should create in a long term perspective rational evidence-based plant medications and should lead to the discovery and development of effective phytomedical interventions in the prevention and treatment of different diseases in form of new single drugs or new combinatory drug regimes, including the understanding and exploitation of synergy-effects.


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(1) Salicylates: esters of salicin like salicortin, tremulacin or 2'-O-acetylsalicortin. These prodrugs are metabolised like salicin to the active drug salicyclic acid.

G. Ulrich-Merzenich (a),*, D. Panek (b), H. Zeitler (b), H. Wagner (c), H. Vetter (a)

(a) Medical Policlinic of the Rheinische Friedrich-Wilhelms-University of Bonn, Wilhelmstr. 35-37, D-53111 Bonn, Germany

(b) Internal Medical Clinic I (CETA) of the Rheinische Friedrich-Wilhelms-University of Bonn, Sigmund-Freud-Str. 25, D-53227 Bonn, Germany

(c) Department of Pharmacy, Centre of Pharma Research, Ludwig-Maximilians-University, Butenandtstr. 5-13, House B, D-81377 Munich, Germany

* Corresponding author. Tel.: +49 228 2872 2674; fax: +49 228 2872 2019.

E-mail address: (G. Ulrich-Merzenich).

doi: 10.1016/j.phymed.2009.04.001
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Author:Ulrich-Merzenich, G.; Panek, D.; Zeitler, H.; Wagner, H.; Vetter, H.
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
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Date:Jun 1, 2009
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