Printer Friendly

Prediction of adverse events by in vivo gene expression profiling exemplified for phytopharmaceuticals containing salicylates and the antidepressant imipramine.

ARTICLE INFO

Article history:

This contribution is dedicated in memoriam to Prof. Dr. Hilke Winterhoff, Institute for Pharmacology and Toxicology, Westfalische Wilhelms-University, Munster, Germany who initiated this work and passed away on May 9, 2010.

Keywords: Adverse events Antidepressant Multitarget Salicylates (Salicin) Imipramine Gene microarray

ABSTRACT

Background and objective: Gene expression profiles of Sprague-Dawley (SD) rats treated with a standardized willow bark extract (WB), its salicin rich ethanol fraction (EtOH-FR) or the tricyclic antidepressant imipramine were evaluated for their potential to induce adverse events. Treatments had shown antidepressant-like effects.

Methods: Gene expression profiles (Agilent Whole Genome Array, n = 4/group) obtained from the peripheral blood of male SD rats treated with WB (STW 33-1), Et0H-FR (30 mg/kg bw) or imipramine (20 mg/kg bw) were analysed comparatively by the Ingenuity Systems Programme, which allows to conduct model calculations of thresholds for theoretical potential adverse events (AE).

Results: The number of genes regulated by the three treatments were 1673 (WB), 117 (EtOH-FR) and 1733 (imipramine). The three treatments related to 47 disease clusters. The WB extract reached the threshold for a potential AE in one disease cluster (cardiac hypertrophy), whereas the EtOH-FR exceeded the threshold in 5 disease clusters (cardiac arteriopathy and stenosis, glomerular injury, pulmonary hypertension, alkaline phosphatase levels fr). Imipramine treatment hit 13 disease clusters: tachycardia, palpitation, myocardial infarction, arrhythmias, heart block, precipitation of congestive heart failure; urinary retention, altered liver functions. Those correspond to known potential adverse events. Glomerular injury and altered liver functions are part of the side effect profile of salicylic acid derivatives in agreement with the findings for the salicin rich EtOH-FR.

Conclusion: There is no linear relationship between the number of constituents of a drug (preparation) and the number of different targets hit in a biological system on the gene expression level. Therefore, the number of genetic targets in a biological system does not necessarily increase with the complexity of the treatment corresponding to the non-linear behaviour of biological systems. Regarding gene expression levels AE of single treatments are not necessarily additive in combination treatments. The applied method appears to be an interesting screening tool for the prediction of potential AE. The phenomena that imipramine crossed the potential threshold for AEs several times whereas the WB extract did reach the threshold level only once, however not backed by clinical data for this AE, deserves to be further investigated. It questions the commonly assumed principle that substances with low number or without AE will have a poor efficacy.

Introduction

The correct prediction of adverse events (AE) of synthetic drugs, phytopharmaceuticals or their combinations is essential for drug development as well as for drugs already in use. In the context of toxicity testing the report by the National Research Council of the US National Academy of Science, Toxicity Testing in the 21st Century has prompted a discussion to base the assessments on "mechanisms and toxicant modes of action" and to identify "pathways of toxicity" (Hartung and McBride 2011). Hartung and McBride (2011) proposed very recently the "Mapping of the human Toxome" as the basis for a new testing approach facilitating the identification of non-toxicity. The OECD coined the term "adverse outcome pathways" (Ankley et al. 2010; Hartung and McBride 2011) to be investigated. We proposed earlier as a rather simple (and first step) approach to apply ready-to-use topic defined Toxgene microarrays as pre-screening tools for phytopharmaceuticals (Ulrich-Merzenich et al., 2009b).

In the present study we used whole genome gene expression profiles, obtained from rats which had shown a positive response in a standard research model for depression -the Por-solt Swimming Test (FST)--and had been treated with either the tricyclic antidepressant imipramine, the standardized willow bark extract STW-33-I (WB), or its salicin rich ethanol fraction (EtOH-FR) (Ulrich-Merzenich et al., 2012) for a theoretical analysis (Ingenuity) for the prediction of AE. Detailed results on the treatmentspecific individual gene regulation are reported under Ulrich-Merzenich et al. (2012).

Here, based on the expression profiles, the prime signalling cascades activated by the different treatments are described and the treatment specific expression profiles are compared with published data of reports about the relation between the modulation of genes and potentially occurring adverse events. For this purpose the Ingenuity programme offers a so-called "toxic endpoint analysis". In this analysis well studied groups of genes/molecules which are known to participate in clinical pathology ( histopathology or clinical chemistry) and to lead to toxicological events or processes within specific tissues are searched for in the submitted gene expression profiles.

The essential parameters in the analysis is the negative logarithm of the p-value (calculated with a right tailed Fisher's exact test) on networks (in the comparison of regulated genes in the different treatments) The higher the score, the less likely the molecules/genes (regarded as tox molecules) within the network are associated due to chance. We finally examined whether the potential targets derived by this evaluation method correspond with the known adverse events of the applied treatments.

Willow bark as originator plant for the development of the non-steroidal anti-inflammatory drugs and its designated principle "salicin" as well as the antidepressant imipramine are known drugs with well documented AE profiles, especially in the case of imipramine. An intensive discussion of their mode of action in the experiments performed here on different genes are reported under Ulrich-Merzenich et al. (2012). This work will concentrate on the analysis of the activated signal cascades, the potential AE and the corresponding genes.

Materials and methods

The dried willow bark preparation STW 33-I (WB) was obtained from Steigerwald Arzneimittelwerk GmbH, Darmstadt, Germany. Extract was prepared from willow bark according to PhEur. 6.1, with a [DEV.sub.nativ] of 16-23:1, total salicin content 23-26% (m/m). Imipramine hydrochloride was obtained from Sigma (Deisenhofen, Germany).

Preparation and characterisation of the tested fractions

The investigated fraction was prepared as described in Freischmidt et al. (2011a) by application of subsequent partition steps using, among other solvents, also ethanol (EtOH-FR). A quantitative and qualitative determination of different classes of compounds in the WB and the resulting fractions was done. The salicin and salicyl alcohol content was determined according Ph.Eur 6.4, total polyphenol, tannin and rest phenol content was quantified according to Glasl (1983). As the flavonoid spectrum of willow bark mainly consists of flavanone and chalcones glycosides the common PhEur methods for determination of overall flavonoid content is not applicable. Thus, it was determined according a newly developed method (Freischmidt et al., 2011b).

The WB is rich in salicin, salicin derivates and polyphenols; whereas the Et0H-FR contains a high amount of salicin and salicin derivates while having comparatively low polyphenol content as described earlier (Ulrich-Merzenich et al., 2012).

Animals

Male Sprague-Dawley (SD) rats (150-170 g, Charles River Labo-ratories, Sulzfeld, Germany) were housed in groups of two and kept in conditioned rooms (24 [+ or -] 1 [degrees]C, light dark cycle 12/12 h). Animals had free access to food (Altromin 1324, Altromin, Lage, Germany) and tap water. The procedures used comply with the European Community's Council Directive of 24 November 1986 (86/609/EEC) and were officially approved by the local committee on animal care (Regierungsprasident Munster, AC/2004).

Test substances

Animals (n =12 per group) received the test solutions (WB, its EtOH-FR, or imipramine, suspended in water, 10 ml/kg b.w.) p. o. once daily as described earlier (Ulrich-Merzenich et al., 2012). The solvent was used as negative control.

Gene microarrays

Blood samples (3 ml) of treated and untreated rats were collected in PAX-gene collection tubes (Preanalytix) 2 h after the performance of the Porsolt Swimming Test (FST). RNA was iso-lated by Pax Gene Blood RNA Kit (Qiagen) and the gene modulation was determined in four animals per group. The RNA-Integrity numbers (Agilent 2100 Bioanalyzer) of the isolated RNAs were between 7.3 and 8.8. Only RNA of treatment groups which showed significant responses in the FST compared to the untreated group were selected for detailed microarray analysis. For analysis single colour hybridization of the rat RNA on the Rat Agilent Whole Genome Oligo Micorarrays (41013 genes) after T7 RNA amplification was performed (Miltenyi Biotec, Bergisch Gladbach, Germany). The Agilent Feature Extraction software (FES) was used to read out and process the microarray image files. For the determination of the differential gene expression FES derived output data files were further analysed using the Rosetta Resolver gene expression data analysis system (Rosetta Biosoftware).The background corrected intensity data were used for the calculation of the ratios control/experimental sample. The ratios were computed using a common "artificial reference" (4 control samples combined). This common reference was used as baseline for all samples. A global correlation analysis of all ratio data was performed. Data sets were filtered in order to remove genes which are not differentially regulated in any comparison.

Data processing and Statistics

Computed ratios were used for further analyses by Ingenuity systems Inc. Redwood City, USA. Data were considered as up - or down-regulation if the expression ratio sample/reference control was [greater than or equal to] 2 with a p-value < 0.01 for the signal cascade analysis. The Gene bank was used as gene identifier and the Ingenuity knowledge base[R] genes were used as gene reference set. Filters were set for mammals for all analyses. With this general setting commonly or differentially regulated genes in each group were determined and the so called canonical pathways (well studied signalling and metabolic pathways within Ingenuity Pathways Analysis (IPA)) were identified.

Benjamini-Hochberg multiple testing correction was used as statistical test for the multiple testing correction to calculate false discovery rate in IPA functions and pathways.

Results

Whole genome micro-arrays

We have earlier reported that rats treated with WB (30 mg/kg bw), its salicin rich EtOH-FR (30 mg/kg bw) and imipramine (20 mg/kg bw) showed a positive response in the FST (Ulrich-Merzenich et al.,2009a).We further have reported about the individual genes modulated by these different treatments (Ulrich-Merzenich et al., 2012).Here we report on the modulated signal cascades, provide a theoretical analysis on the prediction of potential AE and report the number of genes modulated by the different treatments.

Initial data analysis of all ratios revealed that 3974 genes of the WB group, 295 genes of EtOH-FR group and 3079 genes of imipramine group were regulated at least 2 fold (p [less than or equal to] 0.01). The Ingenuity programme recognized somewhat less genes, but with similar proportions. In the WB group 1673 genes were modulated, in the EtOH-FR group--117 genes and in the imipramine group - 1733 genes were different from the control (> 2 fold, p < 0.01) The top up-and down-regulated genes and the commonly regulated genes have been reported earlier (Ulrich-Merzenich et al., 2012).

The five major signal cascades activated by the different treatments are listed in Table 1. Among these no common signal pathway exists which is activated by all three treatments. Each treatment obviously acts via different signal cascades. WB activates primarily the glucocorticoid and phospholipase C signalling, whereas imipramine influences the ECM (chondroitin-sulfate and keratan sulfate) and the salicin rich fraction EtOH-FR activates the hypoxia inducible factor 1 pathway as well as the bladder cancer signalling.

Comparative analysis of the prime signal cascades activated

Name                                             p-Value          Ratio

Croup 1 (WB)
Glucocorticoid receptor signalling              7.28E-05  49/270(0.181)
Phospholipase C signalling                      7.98E-05  45/244(0.184)
Regulation of elF4 and p70S6K signalling        8.79E-05  26/124(0.210)
taveolar-mecliated endocytosis signalling       1.19E-04   20/83(0.241)
EIF2 signalling                                 1.34E-04   22/95(0.232)
Group2(EtOH-FR)
Bladder cancer signalling                       7.64E-04   5/90 (0-056)
HIFlcxsignalling                                1.08E-02   4/107(0.037)
Colorectal cancer metastasis signalling         I.39E-02   6/247(0.024)
Folate biosynthesis                             1-40E-02    2/22(0.091)
11-22 signalling                                2.08E-02    2/27(0.074)
Group3(Imipramine)
Chondroitin sulfate biosynthesis                8.94F-03   10/50(0.200)
LPS/IL-1 mediated inhibition of RXR function    9.42C-03  27/208(0.130)
Keratan sulfate biosynthesis                    2.78E-02   9/51 (0.176)
FXR/RXR activation                              3.66E-02   13/88(0.148)
Estrogen-dependent breast cancer signalling     3.70E-02   10/67(0.149)


Potential adverse events

The expression profiles of the active groups WB, Et0H-FR and imipramine were analysed and compared (Ingenuity programme) for hitting known potential toxicological endpoints/genes in various disease clusters based on published data. Parts of the results are shown in Figs. 1 and 2. WB reached the threshold for the gene cluster "cardiac hypertrophy" but not for any other of the total of 47 clusters. Imipramine exceeded the threshold 13 times for the following disease/symptom clusters: tachycardia, cardiac arythmia, cardiac damage, cardiac hypertrophy, cardiac congestive failure, cardiac dilatation, heart failure, congenital heart anomalie, renal hydronephrosis, renal nephritis, pulmonary hypertension, kidney failure and liver proliferation (Figs. 1 and 2).

The EtOH-FR exceeded the threshold 5 times for cardiac stenosis, glomerular injury, pulmonary hypertension and increased levels of alkaline phosphates (Figs. 1 and 2).

Table 2 summarises the gene expression of each disease cluster on which the potential of adverse events are majorly based.

Table 2
Core genes regulated in each disease cluster above treshold
(see fig. 1 and 2).

Category                      Group       Regulated core
                                          genes

Cardiac hypertrophy           WB          TPM1.FGF23

Cardiac hypertrophy           Imipramine  EPO, UCN2,
                                          TERT, VDR,
                                          ACE

Cardiac arrhythmia            lmipramine  ATP1A3, SCN1A,
                                          KCNH7, KCNG2,
                                          KCNQ1, ADORA1,
                                          ADRA1B, ADRB3,
                                          ADRA1D

Cardiac adenopathy            EtOH-Fr     DLCAP4, MTHFS,
                                          EVC2, MAPK9.
                                          ALOX12,
                                          UBE20.XPR1,
                                          MPPED2
                                          (includes
                                          EC:744),
                                          VEGFA, DLGS.
                                          MATN2, LPL,
                                          PDE5A, CHRNA7,
                                          ROS1, BTBD9,
                                          PSMD1, CDC25A

Cardiac damage                lmipramine  IGF1.C5

Rena! hydronephrosis          Imipramine  ADRA18,
                                          ADRA1D

Renal nephritis               Imipramine  TP53, NOS1,
                                          CTNS, ALB,
                                          NFKB2, VDR,
                                          PDE4D, PPP3CA,
                                          ACE

Heart failure                 lmipramine  EPO, NOS1,
                                          ATP1A3,
                                          CACNA1D,
                                          ACCN3, KCNG2,
                                          PDE4D, SLC9A1,
                                          TTN, ADRB3,
                                          KCNJ11,
                                          ADRA1D.
                                          SCNN1A, ACCN5,
                                          EDNRA, NR3C2,
                                          NPPA, DSP,
                                          ADRA1B, ACE

Cardiac stenosis              EtOH-Fr     SREBF1, PDE5A,
                                          MMP1 (includes
                                          EG:4312)

Liver proliferation           Imipramine  CXCR4.CXCL12,
                                          C5

Tachycardia                   Imipramine  GRIN2B,
                                          ADORA3, KCNH7,
                                          CHRNA7, KCNG2,
                                          KCNQ1, CHRNA5,
                                          ADORA1, CASQ2,
                                          ADRA1B, ADRB3,
                                          ADRA1D

Kidney failure                Imipramine  EPO, ALB,
                                          EDNRA, FKBP1A,
                                          NR3C2, VDR,
                                          PPP3CA

Glomerular injury             EtOH-Fr     SREBF1

Cardiac congestive

Cardiac failure               Imipramine  EPO, NOS1.
                                          CACNA1D,
                                          EDNRA, KCNG2,
                                          NR3C2, PDE4D,
                                          ACE, ADRA1B,
                                          ADRB3, KCNJ11,
                                          ADRAJD

Cardiac dilation              Imipramine  EPO, NOS1

Pulmonary hypertension        EtOH-Fr     EDNRB, FTGER3,
                                          PDE5A

Pulmonary hypertension        Imipramine  EPO, EDNRB,
                                          FLT3, ABL1,
                                          EDNRA, ADRA1B,
                                          ADRB3, ADRA1D

Increased levels of alkaline  EtOH-Fr     VEGFA
phosphatase

Bold indicate that these genes are prevalent in several
disease clusters.


It is interesting to note that some of the genes (ACE, ADRA1D, EDNRA, EPO, NOS1, VDR, VEGF) marked in bold are prevalent in several disease clusters related to different organs (heart, kidney, lung). For abbreviations see: (www.ncbi.nlm.nih.gov.data bases e.g.uniGene).

Discussion

The WB and its salicin rich EtOH-FR contain a high number of potentially active constituents, whereas imipramine is a single chemical entity. It is generally assumed that a higher number of constituents in a drug preparation lead to a higher number of targets and to a higher number of potential AE.

In our in vivo experiments with SD-rats it is interesting to observe that the number of genetic targets hit by the WB treatment or by the treatment with the Et0H-FR was not higher than the number of genetic targets hit by imipramine. Therefore it is reasonable to state that the number of constituents in a treatment does not necessarily have a linear relationship to the number of genetic targets. Considering the complexity of the human metabolism, the putative activated signal cascades, gene networks with their excitatory and inhibitory feed back loops, this result is not surprising. We had already discussed earlier that combination therapies can lead to new modes of action and do not necessarily lead to the addition or multiplication of side effects of the single treatments (Ulrich-Merzenich et al., 2009b). E.g. in rheumatology it is well known that the combination of methotrexate (MTX) and TNF[alpha]-inhibitors (biological) for the treatment of rheumatoid arthritis is superior to the application of the single drug treatment with respect to clinical, radiographic, and functional benefits and notably without increasing AE (Pincus 2003; St Clair et al. 2004). In the meantime, not only TNF[alpha]-inhibitors, but also many other biologicals are administered in combination with MTX because this strongly increases the magnitude and duration of the therapeutic response (Breedveld and Combe 2011; Fiehn 2009). In addition, a recent cost-effectiveness analysis showed that a combination DMARD therapy was likely to be cost-effective compared to DMARD monotherapy (Tosh et al. 2011).

Thus, the often found reluctance towards the use of phytopharmaceuticals may not be justified with respect to multitargeting. In this context we have already earlier taken up a proposal of Sagar et al. (2006), who proposed phytopharmaceuticals as adjuvant treatment for cancer. The scope of co-medications with phytopharmaceuticals showing immunomodulatory, hepato-protective or antiinflammatory properties could be considered (Ulrich-Merzenich et al., 2009b). It can be expected that, even though basic research for the formulation of such treatments is still required, the general approach is promising.

Quietly, the multitarget approach is practised not only in the field of rheumatology, but also generally in the treatment of diseases which are regarded as multifactorial diseases.

The most advanced research for combination therapies is presently undertaken for cardiovascular diseases and hypertension. Here, the development of the so-called "polypill"--a fixed combination of agents addressing various components of the metabolic syndrome as well as other coexisting common risk factors has been proposed already many years ago as first-line treatment for hypertension (Gavras and Rosenthal 2004). Encouraging results demonstrate also here that fixed dose combination therapy can offer potential advantages over individual agents, including increased efficacy and reduced incidence of AE as well as lower healthcare costs and improved patients compliance (Garcia-Donaire and Ruilope 2010).

Synergistic actions, which are defined as true over additive effects (Wagner 2011; Wagner and Ulrich-Merzenich, 2009), are considered as the underlying mode of action. Synergism can be understood as the expression of feed back loops of signal cascades in the metabolism which subsequently leads to new and potentially synergistic modes of actions.

The following signal cascades are activated by the different treatments applied in our SD-rats:

Signal cascades

Willow bark preparation

WB is a well known anti-inflammatory and immunosuppressive drug inhibiting the cyclo-oxygenases. It is not astonishing (has been shown earlier) that WB can act via G-protein signalling and in our experiments via activating phospholipase C, which leads to the formation of IP3 (phosphatidyl-inositol-1,4,5 triphosphate) and DAG (diacylglycerol). Astonishing is, however, that signalling related to glucocorticoids is listed as primary signalling way. This phenomena deserves to be further investigated.

Membrane domains play an active role in directing or redirecting G protein signals (Golebiewska and Scarlata 2010). The most stable membrane domains are caveolae. Consequently, if WB acts via G-proteins it is likely that caveolar-mediated endocytosis signalling is involved.

WB can obviously also be involved in cell growth. The mRNA binding translation factors (eIF4s) selectively regulate the initiation of translation of different mRNAs (Flynn and Proud 1996). The P70S6K signalling appears to play a regulatory role in this system via 4EBP1. Targeting the translational machinery as a novel treatment strategy for haematological malignancies has been proposed recently (Hagner et al. 2010).

Salicin and salicin derivate rich fraction

The major signal cascades involved in the EtOH-FR treatment are grossly related to proliferation and apoptosis indicated by the so called "bladder cancer and colorectal cancer" signalling in combination with the modulation of the folate biosynthesis. Genes are related to cell cycle and apoptosis regulators.

The signalling pathway of the hypoxia inducible factor-1 (HIF) is discussed as potential target for cancer chemotherapy, chemosen-sitization and chemoprevention (Monti and Gariboldi 2011). HIFI is a nuclear transcription factor that is up regulated in hypoxia and co-ordinates the adaptive response to hypoxia by driving the expression of over 100 genes (Kasivisvanathan et al. 2011). Thus, HIFI and its pathway is also discussed as putative therapeutic tar-get in diseases like hypertension, stent-restenosis, stroke or PAD, all diseases in which a ischemic injury occurs. Targeting the HIF-1 signalling pathway via a phytopharmaceutical with an antioxidative activity is not surprising, but should be further investigated.

The influence on IL-22 signalling is likely to be seen in the context of the anti-inflammatory activity of the EtOH-FR. Studies of mouse model systems have identified a critical role for signalling by IL-22 through its receptor (IL.22R) in the promotion of inflammation and tissue repair at barrier surfaces (Sonnenberg et al. 2011). IL-22 can also disrupt tight junction proteins in endothelial cells of the central nervous system (Jadidi-Niaragh and Mirshafiey 2011).

IL-22 is a cytokine which belongs to the IL-10 family (Ouyang 2010). It is expressed at barrier surfaces and its expression is deregulated in certain human diseases suggesting a critical role in the maintenance of normal barrier homeostasis (Sonnenberg et al. 2011).

Imipramine

Imipramine influences importantly the biosynthesis pathways of two ECM-molecules--chondroitin and heparan sulfate. The proteoglycan chondroitin sulfate is an essential component of hip-pocampal ECM co-localized in perineuronal nets on interneurons (Bukalo et al. 2001). It was demonstrated in mice that chon-droitin sulfate proteoglycan can differentially modulate several forms of synaptic plasticity relating it to depression (Bukalo et al. 2001). Therefore it is not astonishing that an antidepressant like imipramine is related to this "signalling cascades". The sulfation pattern of CS is also regarded as a key player in protein interaction causing atherosclerosis (Karangelis et al. 2010).

The LPS/IL-1[beta] signalling pathway relates to the activation of the CD14/TRL4/MD2 receptor complex, which promotes the secretion of pro-inflammatory cytokines (IL-1, TNF-alpha) in different cell types but especially macrophages. We have earlier shown that imipramine down regulates proinflammatory cytokines (Ulrich-Merzenich et al., 2012). Thus, these results are consistent with those findings.

Retinoid x receptor (RXR) signalling takes place in the presence of cytokines, e.g. during proinflammatory cytokine signalling. RXR[alpha] undergoes a JNK-mediated, CRM-1-dependent nuclear export, leading to decreased nuclear RXRa levels, reduced DNA binding and transcriptional activity (Ingenuity).

RXR receptor signalling is also discussed as useful pharmacological target to overcome the remyelination failure in MS (associated with oligodendrocyte precursor cells) (Huang and Franklin 2011).

Estrogen dependent breast cancer signalling is implicated for imipramine. The central role of estrogen and the estrogen receptor (ER) pathway in the development and progression of hormone dependent human breast cancer has been established for decades (Baumann et al. 2009; Beatson 1896; Clarke et al. 1994, 2004; Dickson et al. 1990; Simpson et al. 2005). ER activation in human breast cancer cells leads to an increase in cell proliferation mediated at least in part by a higher transcription and bioavailability of growth factors (Baumann et al. 2009; Dickson et al. 1990). Modula-tion of intracellular signalling pathways by ER activation influences further molecular mechanisms in favour of cell survival (Baumann et al. 2009; Butt et al. 2005).

Even though WB targets a broad spectrum of molecules due to activating genes known to be activated through the glucocorti-coid signalling pathway--it is interesting to note that the common AE reported for glucocorticoids do not show up--at least on this theoretical based analysis.

Potential adverse events

It is astonishing to note that based on the comparative analysis, the WB extract did only reach the threshold for one potential adverse event in the disease cluster cardiac hypertrophy, whereas imipramine exceeded this threshold in several cases. One is tempted to question whether the century old claims that natural medicine have less side effects than synthetic drugs have a reasoned scientific base. Since the number of genetic targets hit by the three groups do not correspond to the number of potential adverse events, the combination of the targets or the strength of the insults seem to determine this result. Nevertheless, the phenomenon needs to be investigated in more detail to make sure that it is not an artefact based on the presently still incompleteness of available data bases. At the same time, comparing the "disease clusters" hit by imipramine with the reported side effects for imipramine reveals a partial matching: cardiovascular side effects like tachycardia, palpitation, myocardial infarction, arrhythmias, heart block and precipitation of congestive heart failure as well as urinary retention and altered liver functions are reported side effects.

Short-term oral, inhalation, and parenteral exposures to salicylates sufficient to produce high blood concentrations are associated primarily with liver and kidney damage (Safety assessment 2003) in agreement with the findings for the salicin rich EtOH-FR.

Subchronic dermal exposures to undiluted methyl salicylate were associated with kidney damage. Chronic oral exposure to methyl salicylate produced bone lesions as a function of the level of exposure in 2-year rat studies; liver damage was seen in dogs exposed to 0.15 g/kg/day in one study; kidney and liver weight increases in another study at the same exposure; but no liver or kidney abnormalities in a study at 0.167 g/kg/day (Safety assessment 2003).

Contrary, clinical long term data have shown a lack of severe AE for the WB extract in osteoarthritis and back pain (Muller et al. 2010).

It is interesting to note that a number of common genes occur irrespective of the organ (heart, kidney, lung) effected in the context of adverse events: erythropoetin (EPO), nitric oxide synthase 1 (NOS1), angitotensin converting enzyme (ACE), vascular endothelial growth factor (VEGFA), endothelin receptor (EDNRA) and the adrenergic [alpha]-1D-receptor (ADRA1D). Most of these genes are well known and involved in central/fundamental functions like EPO, regulating the red cell production, NOS1 involved in the nitric oxide synthesis, ACE involved in the blood pressure regulation and VEGF as proangiotic factor. More details about the individual genes are given below:

Erythropoietin (EPO)

This gene is a member of the EPO/TPO family and is found in the plasma. It regulates red cell production by promoting erythroid differentiation and initiating hemoglobin synthesis. This protein also has neuroprotective activity against a variety of potential brain injuries and antiapoptotic functions in several tissue types (RefSeq EPO).

Vitamin D (1,25-dihydroxyvitamin D3) receptor (VDR)

This gene encodes the nuclear hormone receptor for vitamin D3.

The receptor belongs to the family of trans-acting transcriptional regulatory factors and shows sequence similarity to the steroid and thyroid hormone receptors. Downstream targets of this nuclear hormone receptor are principally involved in mineral metabolism though the receptor regulates a variety of other metabolic pathways, such as those involved in the immune response and cancer. Mutations in this gene are associated with type II vitamin D-resistant rickets. Alternative splicing results in multiple transcript variants encoding different proteins (RefSeq VDR).

Nitric oxide synthase 1 (neuronal) (NOS1)

The protein encoded by this gene belongs to the family of nitric oxide synthases, which synthesize nitric oxide from L-arginine. Nitric oxide is a reactive free radical, which acts as a biologic mediator in several processes, including neurotransmission as well as antimicrobial and antitumoral activities. In the brain and peripheral nervous system, nitric oxide displays many properties of a neurotransmitter, and has been implicated in neurotoxicity associated with stroke and neurodegenerative diseases, neural regulation of smooth muscle, including peristalsis, and penile erection. This protein is ubiquitously expressed. Alternatively spliced transcript variants encoding different isoforms (some testis-specific) have been found for this gene (RefSec NOS1).

Angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 (ACE)

This gene encodes an enzyme involved in catalyzing the conversion of angiotensin I into a physiologically active peptide angiotensin II. Angiotensin II is a potent vasopressor and aldosterone-stimulating peptide that controls blood pressure and fluid-electrolyte balance. This enzyme plays a key role in the renin-angiotensin system. Multiple alternatively spliced transcript variants encoding different isoforms have been identified, and two most abundant spliced variants encode the somatic form and the testicular form, respectively, that are equally active (RefSeq ACE).Vascular endothelial growth factor A (VEGF)

This gene is a member of the PDGF/VEGF growth factor family and encodes a protein that is often found as a disulfide linked homodimer. This protein is a glycosylated mitogen that specifically acts on endothelial cells and has various effects, including mediating increased vascular permeability, inducing angiogenesis, vasculogenesis and endothelial cell growth, promoting cell migration, and inhibiting apoptosis. Elevated levels of this protein are linked to POEMS syndrome, also known as Crow-Fukase syndrome. Mutations in this gene have been associated with proliferative and nonproliferative diabetic retinopathy. Alternatively spliced transcript variants, encoding either freely secreted or cell-associated isoforms, have been characterised. There is also evidence for the use of non-AUG (CUG) translation initiation sites upstream of, and in-frame with the first AUG, leading to additional isoforms (RefSeq VEGFA). VEGFA, a known proangiogenic factor, plays a very important role in pulmonary vascular development (Burgos et al. 2010). Imipramine and EtOH-FR lead to a down regulation and simultaneously to a significant down regulation of TIMP3. These findings were considered for judging both as relevant for pulmonary hypertension.

Endothelin receptor type A (EDNRA)

This gene encodes the receptor for endothelin-1, a peptide that plays a role in potent and long-lasting vasoconstriction. This receptor associates with guanine-nucleotide-binding (G) proteins, and this coupling activates a phosphaticlylinositol-calcium second messenger system. Polymorphisms in this gene have been linked to migraine headache resistance. Alternative splicing results in multiple transcript variants (RefSeq EDNRA).

Adrenergic, alpha-ID-, receptor (ADRA1D)

Alpha-1-adrenergic receptors (alpha-1 -ARs) are members of the G protein-coupled receptor superfamily. They activate mito-genic responses and regulate growth and proliferation of many cells. There are 3 alpha-1-AR subtypes: alpha-1A, -1B and -1D, all of which signal through the Gq/11 family of G-proteins and different subtypes show different patterns of activation. This gene encodes alpha-1 D-adrenergic receptor. Similar to alpha-1B-adrenergic receptor gene, this gene comprises 2 exons and a single intron that interrupts the coding region (RefSeq ADRA1D).

The above mentioned genes should be further investigated for their potential role in the development of adverse events and in the context of their relevance for the human "Toxome".

Limitations

Our first data present the analysis of only one concentration for each group. However, the dosage dependency of adverse events needs to be considered in future experiments.

The analysis for potential adverse events even though based on in vivo data is presently still a theoretical exercise strongly depending on the data base used for such an analysis. The presented data base offers three types of analyses for determining "Tox"-or adverse events: one is based on molecules, a second one is based on processes and a third one is based on core molecules relevant for clinical pathology endpoints. Core molecules are compounds that after meeting the cut off values, have at least one other interaction with another gene, protein, or small molecule in the knowledge data base. Since the clinical pathology endpoint analysis is the most informative analysis for adverse events we presented those results. However, the differentiation between molecules, processes and the decision on clinical pathology endpoints delivers not always coherent results obviously due to overlapping assignments of the genes to the categories "processes or molecules" and due to different selection criteria for the results to become relevant for the process/network-analysis. At the same time the available world wide data banks are likely to have a bias in the direction of "cancer signalling" since oncology is the most prominent research area. Nevertheless the data base is increasing daily with more and more array data being entered allowing an increasingly precise picture on molecule networks, their cross signalling and their final relevance for biological functions including adverse events.

In summary, the number of genetic targets influenced by WB, a complex mixture of a multitude of components, was not substantially higher than those influenced by imipramine, a single chemical entity. Therefore the number of targets in a biological system does not necessarily depend on the complexity of the treatment. This corresponds with the principle of a non-linear behaviour of biological systems. The applied method appears to be useful for the prediction of potential AE and deserves to be further evaluated. The phenomenon that the clinically effective WB extract reached only once the threshold level for an AE, whereas imipramine crossed this threshold several times should be further investigated. It questions the commonly assumed principle that substances without or with a low number of AE will have a poor efficacy.

Acknowledgements

Authors wish to thank Bernd Merzenich for editorial assistance and are thankful to the Steigerwald Arzneimittelwerk GmbH which contributed financially to the study. Dr. Anna Koptina was supported by the German Academic Exchange Service (DAAD).

Abbreviations: 4EBP1, eukaryotic translation initiation factor 4E binding protein 1; ADRA1D, adrenergic a-1 D-receptor; AE, adverse events; ACE, angiotensin I converting enzyme (peptidyl-dipeptidase A) 1; SD, Sprague-Dawley; DAG, diacylglycerol; DMARD, disease modifying anti-rheumatic drug; ECM, extra cellular matrix; EDN RA, endothelin receptor; elF4s, mRNA binding translation factors; EPO, erythropoietin; ER, estrogen receptor; EtOH-FR, ethanol fraction; FES, Agilent Feature Extraction software; EST, Porsolt Swimming Test; G protein, guanine-nucleotide-binding protein; HIF, hypoxia inducible factor-1; IP3, phosphatidyl-inositol-1,4,5 triphosphate; WA, Ingenuity Pathways Analysis; JNK, c-Jun N-terminal kinase; MS, multiple sclerosis; MTX, methotrexate; NOS1, nitric oxide synthase 1; PAD, peripheral arterial disease; PDGF, platelet-derived growth factor beta; RXR, retinoid x receptor; TIMP3, tissue inhibitor of metal loproteinase 3; VDR, vitamin D (1,25-dihydroxyvitamin D3) receptor; VEGF, vascular endothelial growth factor; WB, willow bark extract.

* Corresponding author. Tel.: +49 22828722674; fax: +49 22828722266.

E-mail address: gudrun.ulrich-merzenich@ukb.uni-bonn.de (G. Ulrich-Merzenich).

References

Ankley, G.T., Bennett, R.S., Erickson, RI., Hoff, D.J., Hornung, M.W., Johnson, R.D., Mount, D.R., Nichols, J.W., Russom, CL., Schmieder, P.M., Serrrano, J.A., Tietge, J.E., Villeneuve, D.L., 2010. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 29 (3), 730-741.

Baumann, K.H., Klusmeier, E., Eggemann, I., Reinartz, S., Almeroth, A., Kalder, M., Wagner, U., 2009. Effects of celecoxib and ly117018 combination on human breast cancer cells in vitro. Breast Cancer (Auckl) 3, 23-34.

Beatson, G., 1896. On the treatment of inoperable cases of carcinoma of the mamma: suggestions for a new method of treatment with illustrative cases. Lancet ii, 104-107.

Breedveld, Combe, B., 2011. Understanding emerging treatment paradigms in rheumatoid arthritis. Arthritis Res. Ther. 13 (Suppl. 1), S3.

Bukalo, O., Schachner, M., Dityatev, A., 2001. Modification of extracel I u la r matrix by enzymatic removal of chondroitin sulfate and by lack of tenascin-R differentially affects several forms of synaptic plasticity in the hippocampus. Neuroscience 104 (2), 359-369.

Burgos, C.M., Uggla, A.R., Fagerstrom-Billai, F., Eklef, A.-C., Frenckner, B., Nord, M., 2010. Gene expression analysis in hypoplastic lungs in the nitrofen model of congenital diaphragmatic hernia. J. Pediatr. Surg. 45 (7), 1445.

Butt, A.J., McNeil, C.M., Musgrove, E.A., Sutherland, R.L., 2005. Downstream targets of growth factor and oestrogen signalling and endocrine resistance: the potential roles of c-Myc, cyclin D1 and cyclin E.. Endocr. Relat. Cancer 12 (Suppl. 1), 547-559.

Clarke, R., Anderson, E., Howell, A., 2004. Steroid receptors in human breast cancer. Trends Endocrinol. Metab. 15.316-323.

Clarke, R., Skaar, T., Baumann, K., Leonessa, F., James, M., Lippmani., Thompson, E.W., Freter, C., Brunner, N., 1994. Hormonal carcinogenesis in breast cancer: cellular and molecular studies of malignant progression. Breast Cancer Res. Treat. 31 (2-3), 237-248.

Dickson, R., Thompson, E., Lippman, M., 1990. Regulation of proliferation, invasion and growth factor synthesis in breast cancer by steroids. J. Steroid Biochem. Mol. Biol. 37,305-316.

Fiehn, C., 2009. Methotrexate in rheumatology. Z. Rheumatol. 68 (9), 747-756, quiz 757.

Flynn, A., Proud, C.G., 1996. The role of eIF4 in cell proliferation. Cancer Surv. 27, 293-310.

Freischmidt, A., Jurgenliemk, G., Kraus, B., Okpanyi, S., Muller, J., Kelber, 0., Weiser, D., Heilmann, J., 2011. Contribution of flavonoids and catechol to the reduction of ICAM-1 expression in endothelial cells by a standardized willow bark extract. Phytomedicine, 2011 Oct 5 [ahead of print].

Freischmidt, A., PhD thesis, Universitat Regensburg, 2011. Phytochemische, analytische and pharmakologische in vitro Untersuchungen zu den phenolischen I nhaltsstoffen eines standardisierten Weidenrindenextraktes.

Garcia-Donaire, J.A., Ruilope, L.M., 2010. Multiple action fixed combination. Present or future? Fundam. Clin. Pharmacol. 24 (1), 37-42.

Gavras, L, Rosenthal, T., 2004. Combination therapy as first-line treatment for hypertension. Curr. Hypertens. Rep. 6 (4), 267-272.

Glasl, H., 1983. Zur Photometrie in der Drogenstandardisierung-3. Gehaltsbestimmung von Gerbstoffdrogen. DAZ (123), 1979-1987.

Golebiewska, U., Scarlata, S., 2010. The effect of membrane domains on the G proteinphospholipase Cbeta signalling pathway. Crit. Rev. Biochem. Mol. Biol. 45 (2), 97-105.

Hagner, P.R.. Schneider, A., Gartenhaus, R.B., 2010. Targeting the translational machinery as a novel treatment strategy for hematologic malignancies. Blood 115 (11), 2127-2135.

Hartung, T., McBride, M., 2011. Food for thought. On mapping the human toxome. Altex 28 (2/11), 83-93.

Huang, J.K., Franklin, R.J., 2011. Regenerative medicine in multiple sclerosis: identifying pharmacological targets of adult neural stem cell differentiation. Neurochem. Int. 59 (3), 329-332.

Jadidi-Niaragh, F., Mirshafley, A., 2011. Th17 cell, the new player of neuroinflammatory process in multiple sclerosis. Scand. J. Immunol. 74 (1), 1-13.

Karangelis, D.E., Kanakis, I., Asimakopoulou, A.P., Karousou, E., Passi, A., Theocharis, A.D., Triposkiadis, F., Tsilimingas, N.B., Karamanos, N.K., 2010. Glycosaminoglycans as key molecules in atherosclerosis: the role of versican and hyaluronan. Curr. Med. Chem. 17 (33), 4018-4026.

Kasivisvanathan, V., Shalhoub, J., Lim, C.S., Shepherd, A.C., Thapar, A., Davies, A.H., 2011. Hypoxia-inducible factor-1 in arterial disease: a putative therapeutic target. Curr. Vasc. Pharmacol. 9 (3), 333-349.

Monti, E., Gariboldi, M.B., 2011. HIF-1 as a target for cancer chemotherapy, chemosensitization and chemoprevention. Curr. Mol. Pharmacol. 4 (1), 62-77.

Muller, J., Kelber, 0., Weiser, D., Stange, R., Uehleke, I3., 2010. Willow bark extract STW 33-I in the long term treatment of osteoarthritic and back pain. Planta Med. 76,136.

Ouyang, W., 2010. Distinct roles of IL-22 in human psoriasis and inflammatory bowel disease. Cytokine Growth Factor Rev. 21 (6), 435-441.

Pincus. T., 2003. Guidelines for monitoring of methotrexate therapy: evidence-based medicine outside of clinical trials. Arthritis Rheum. 48 (10), 2706-2709.

RefSec NOS1. http://www.ncbi.nlimnih.gov/gene/4842.

RefSeq ACE. http://www.ncbisilm.nih.govigene/1636.

RefSeq ADRA1D. http://www.ncbi.nlm.nih.gov/gene/146.

RefSeq EDNRA. http://www.ncbi.nlm.nih.govigene/1909.

RefSeq EPO. http://www.ncbi.nlm.nittgov/gene/2056.

RefSeq VDR. http://www.ncbi.nIntnih.govigene/7421.

RefSeq VEGFA. http://www.ncbi.nlm.nih.gov/gene/7422.

Safety assessment, 2003. Safety assessment of salicylic acid, butyloctyl salicylate, calcium salicylate, C12-15 alkyl salicylate, capryloyl salicylic acid, hexyldodecyl salicylate, isocetyl salicylate, isodecyl salicylate, magnesium salicylate, MEAsalicylate, ethylhexyl salicylate, potassium salicylate, methyl salicylate, myristyl salicylate, sodium salicylate, TEA-salicylate, and tridecyl salicylate. Int. J. Toxicol. 22 (Suppl. 3), 1-108.

Sagar, S.M., Yance, D.. Wong, LK., 2006. Natural health products that inhibit angiogenesis: a potential source for investigational new agents to treat cancer. Part 1. Curr. Oncol. 13 (1), 14-26.

Simpson, RT., Reis-Filho, J.S., Gale, T., Lakhani, S.R., 2005. Molecular evolution of breast cancer. J. Pathol. 205 (2), 248-254.

Sonnenberg, G.F., Fouser, LA., Artis, D., 2011. Border patrol: regulation of immunity, inflammation and tissue homeostasis at barrier surfaces by IL-22. Nat. Immunol. 12 (5), 383-390.

St Clair, E.W., van der Heijde, D.M., Smolen, J.S., Maini, R.N., Bathon, J.M., Emery, P., Keystone, E., Schiff, M., Kalden, J.R., Wang, B., Dewoody, K., Weiss, R., Baker, D., 2004. Combination of infliximab and methotrexate therapy for early rheumatoid arthritis: a randomized, controlled trial. Arthritis Rheum. 50 (11), 3432-3443.

Tosh, J.C., Wailoo, kJ., Scott, D.L., Deighton, C.M., 2011. Cost-effectiveness of combination nonbiologic disease-modifying antirheumatic drug strategies in patients with early rheumatoid arthritis. J. Rheumatol. 38 (8), 1593-1600.

Ulrich-Merzenich, G., Kelber, 0., Koptina, A., Freischmidt, A., Hellmann, J., Muller, J., Zeitler, H., Seidel, M., Ludwig, M., Heinrich, E., Winterhoff, H., 2012. Standardized

willow bark preparation shows antidepressant like effects. Phytomedicine, in press.

Ulrich-Merzenich, G., Panek, D., Zeitler, H., Heilmann, J., Freischmidt, A., Kelber, 0., Muller, J., Winterhoff, H., 2009a. Gene expression profiling of blood cells from rats treated with different fractions of standardized aqueous willow bark extract-evidence for immuno-and neuroimmunomodulatory activities. Planta Med. 75 (898), 5153.

Ulrich-Merzenich, G., Panek, D., Zeitler, H., Wagner, H., Vetter, H., 2009b. New perspectives for synergy research with the omit-technologies. Phytomedicine 16 (6-7), 495-508.

Wagner, H., 2011. Synergy research: approaching a new generation of phytopharmaceuticals. Fitoterapia 82 (1 ), 34-37.

Wagner, H., Ulrich-Merzenich, G., 2009. Synergy research: approaching a new generation of phytopharmaceuticals. Phytomedicine 16 (2-3), 97-110.

G. Ulrich-Merzenich (a), *, A. Koptina (a), (b), O. Kelber (c), A. Freischmidt (d), J. Hellmann (d), J. Mailer (c), F. Sadeghlar (e), H. Zeitler (e), H. Wagner (f)

(a.) Medical Clinic III, Universitdisklinikum, Rheinische Friedrich-Wilhelms-University Bonn, D-53111 Bonn, Germany

(b.) Mari State Technical University, Yoshkar-Ola, Russia

(c.) Steigeywald Arzneimittelwerk GmbH, D-64295 Darmstadt, Germany

(d.) Pharmazeutische Biologie, Universittit Regensburg, 93040 Regensburg, Germany

(e.) Medical Clinic 1, Universitiltsklinikum, Rheinische Friedrich-Withelms-University Bonn, D-531 H Bonn, Germany

(f.)Department of Pharmacy, Centre of Pharrna-Research, University of Munich, Butenandstr 5-13, 0-81377 Munich, Germany

0944-71 13/$--see front matter [c] 2011 Elsevier GmbH. All rights reserved.

doi:10.1016/j.phymed.2011.09.078
COPYRIGHT 2012 Urban & Fischer Verlag
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2012 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ulrich-Merzenichm, G.; Koptina, A.; Kelber, O.; Freischmidt, A.; Heilmann, J.; Muller, J.; Sadeghlar
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Article Type:Report
Geographic Code:4EUGE
Date:Mar 1, 2012
Words:6798
Previous Article:Neuroprotective iridoid glycosides from Cornus officinalis fruits against glutamate-induced toxicity in HT22 hippocampal cells.
Next Article:Tanshinone IIA and tanshinone I production by Trichoderma atroviride D16, an endophytic fungus in Salvia miltiorrhiza.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters