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The introduction of the concept of systems biology, enabling the study of living systems from a holistic perspective based on the profiling of a multitude of biochemical components, opens up a unique and novel opportunity to reinvestigate natural products. In the study of their bioactivity, the necessary reductionistic approach on single active components has been successful in the discovery of new medicines, but at the same time the synergetic effects of components were lost. Systems biology, and especially metabolomics, is the ultimate phenotyping. It opens up the possibility of studying the effect of complex mixtures, such as those used in Traditional Chinese Medicine, in complex biological systems; abridging it with molecular pharmacology. This approach is considered to have the potential to revolutionize natural product research and to advance the development of scientific based herbal medicine. Copyright © 2005 John Wiley & Sons, Ltd.

This review discusses some of the recent developments in genomics and its current and future relevance for veterinary pharmacology and toxicology. With the rapid progress made in this field several new approaches in pharmacological and toxicological research have developed and drug discovery and drug development strategies have changed dramatically. In this review, the term genomics is used to encompass the three sub-disciplines transcriptomics, proteomics and metabolomics (or metabonomics) to describe the formation and fate of mRNA, proteins and metabolites, respectively. The current status and methods of the technology and some applications are briefly described. Although the DNA sequencing programmes are receiving considerable attention, the real value of genomics for pharmacology and toxicology is brought by the parallel developments in bio-informatics, bio-statistics and the integration of biology with mathematics and information technology. The ultimate level of integration is now mostly called systems biology, where mRNA, proteins and metabolites are being analysed in parallel, using a complete arsenal of analytical techniques (DNA-array, LC-MS/MS, GC-MS/MS, NMR, etc.). The information thus collected is analysed, integrated, linked to database information and translated to pathways and systems. This approach offers an enormous potential to study disease mechanisms and find new drug targets. Thus far, genomics and systems biology have not been introduced significantly in typical veterinary pharmacological and toxicological research programmes. The high costs and complexity connected to these large projects often form major obstacles for research groups with limited budgets. In other veterinary areas and disciplines, including infectious diseases, animal production and food-safety more examples of application are available. Genomics and bio-informatics provide outstanding opportunities to study pharmacology and toxicology in a more holistic way, taking into account the complexity of biological systems and based on the basic principles of physiology and the concept of homeostasis. Knowledge of biology, in vivo and in vitro models, and comparative pharmacology/toxicology is essential here, creating excellent opportunities for the veterinary trained scientist. © 2005 Blackwell Publishing Ltd.

The discovery of new anti-inflammatory drugs is often based on an interaction with a specific target, although other pathways often play a primary or secondary role. Anti-inflammatory drugs can be categorized into classes, based on their mechanism of action. In this article we investigate the possibility to characterize novel anti-inflammatory compounds by three holistic methods. For this purpose, we make use of macrophage-like U937 cells which are stimulated with LPS in the absence or presence of an anti-inflammatory compound. Using micro-arrays, 2-D gel electrophoresis and a LC-MS method for lipids the effects on the transcriptome, proteome and metabolome of the exposed cells is investigated. The expression patterns are subsequently analyzed using in-house developed pattern recognition tools. Using the methods described above, we have examined the effects of six anti-inflammatory compounds. Our results demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorise known molecules and to discover and classify new leads. The potential of our approach is illustrated by the analysis of several beta (2)-adrenergic agonists (β2-agonists). In addition to their primary pharmacological target, β2-agonists posses certain anti-inflammatory properties. We were able to show that zilpaterol, a poorly characterized β2-agonist, gives rise to an almost identical expression pattern as the β2-agonists clenbuterol and salbutamol. Furthermore we have identified specific mRNA, protein and lipid markers for the anti-inflammatory compounds investigated in this study. © 2004 Elsevier B.V. All rights reserved. Chemicals/CAS: 4 (4 fluorophenyl) 2 (4 methylsulfinylphenyl) 5 (4 pyridyl)imidazole, 152121-47-6; clenbuterol, 21898-19-1, 37148-27-9; dexamethasone, 50-02-2; formoterol, 73573-87-2; salbutamol, 18559-94-9; Adrenergic beta-Agonists; Anti-Inflammatory Agents; Lipids; lipopolysaccharide, Escherichia coli 0111 B4; Lipopolysaccharides; Proteome; Receptors, Adrenergic, beta-2; RNA, Messenger

On the basis of systems thinking, in this thesis metabolomics, Chinese medicine (CM), as well as Western medicine (WM) were combined to achieve a more comprehensive systems diagnosis of patients with chronic diseases. Specifically, metabolomics has been applied to characterize patients by small molecule profiles, which can be applied in phenotyping patients and matching these with optimal therapies. Chinese and Western medicine have different perspectives on diagnosis and could be highly complementary to each other, so combining both could have advantages in personalized diagnosis. Therefore, in this thesis a combination of systems approaches is used, including metabolomics, CM-based diagnosis principles as well as WM to provide systems diagnosis of patients with chronic diseases, in particular rheumatoid arthritis (RA). With systems diagnosis, we could better identify potential biomarkers to predict the WM therapeutic response of patients with RA and monitoring disease progression.

Despite the rich diversity in molecular structures and biological functionality, the complex matrix of the compounds with a broad dynamic range has limited the use of plants as an important source for drug development. Adressing this issue, several papers have described the use of the more holistic approach targeting on a wide range of metabolite present in plant extract, i.e. metabolomics. This new approach involves the use of various analytical methods followed by appropriate multivariate data analysis. The aim is to shorten the bioassay guided isolation route, particularly in the identification and dereplication step This thesis describes a new strategy to improve dereplication and identification steps in drug discovery process from plants. The integration of comprehensive extraction coupled to NMR metabolomics and multivariate data analysis is found to be a potential new approach to uncovering bioactive compounds from crude plant extracts.

The introduction of the 'omics' techniques (transcriptomics, proteomics, and metabolomics) and systems biology, has caused fundamental changes in the drug discovery process and many other fields in the life science area. In this thesis we explored the possibilities to apply these holistic technologies to investigate the effects of known and potential anti-inflammatory compounds on macrophages. For this purpose we made use of a monocyte-like human histocytic lymphoma cell line U937. U937 cells can be induced by phorbol 12-myristate 13-acetate (PMA) to undergo differentiation into a macrophage-like phenotype. The two differentiation stages, monocyte and macrophage, were compared by using oligonucleotide microarrays and 2-D gel electrophoresis in combination with principal component analysis (PCA). This differentiation study is described in Chapter 2. The differential expression of three protein biomarkers, gamma interferon inducible lysosomal thiol reductase (GILT), cathepsin D and adipocyte-fatty acid binding protein (A-FABP) were biologically validated by Western blot and real time polymerase chain reaction (real time PCR). GILT and A-FABP were also found to be differentially expressed at the mRNA level as indicated by the results of the microarray experiment. Moreover, the transcriptomics data revealed a large number of additional putative differentiation markers in U937 macrophages, many of which are known to be expressed in peripheral blood-derived macrophages. From the results presented in Chapter 2 can be concluded that the U937 cell line is an excellent model system for the blood-derived macrophage and that microarrays and 2-D gel electrophoresis are suitable methods to identify biomarkers for differentiation. Chapter 3 describes the use of a systems biology approach to categorize anti-inflammatory drugs based on their mRNA, protein and lipid expression pattern, as determined by oligonucleotide microarrays, 2-D gel electrophoresis and a LC-MS method for lipids, in combination with principal component discriminant analysis (PC-DA). The results described in this chapter demonstrate that different classes of anti-inflammatory compounds show distinct and characteristic mRNA, protein, and lipid expression patterns, which can be used to categorize known anti-inflammatory drugs, as well as to discover and classify new leads. The latter was exemplified by the categorization of zilpaterol, a poorly characterized ____-agonist. Exposure to zilpaterol gives rise to an almost identical expression pattern as that observed after exposure to the well-characterized __2-agonists clenbuterol and salbutamol, suggesting that zilpaterol is indeed a ____-agonist. In addition, this study revealed potential biomarkers for the different anti-inflammatory drugs under investigation. The categorization of the anti-inflammatory drugs on the basis of proteomics data alone was not successful. The most likely explanation for this is that by the analysis of whole cell lysates, only highly abundant proteins can be visualized, while the low abundant proteins, which are often involved in important metabolic pathways, are not. Therefore, a more focused approach was used to investigate the mechanism of action of zilpaterol, which is described in Chapter 4. In Chapter 4, U937 macrophages were stimulated with LPS to induce an inflammatory response. This response was inhibited by the addition of zilpaterol (LZ) and this inhibition was antagonized by the _2-adrenergic receptor antagonist propranolol (LZP). Two-dimensional difference gel electrophoresis (DIGE) in combination with Student__s t-test and two multivariate data analysis tools (PCA and partial least squares discriminant analysis PLS-DA) were used to examine the secreted proteome induced by the three treatments. This revealed 8 potential protein biomarkers. The protein spots were identified using nano LC-MS-MS. Only two of the identified proteins, namely macrophage inflammatory protein-1_ (MIP-1_) and macrophage inflammatory protein-1_ (MIP-1_) are known to be secreted proteins. The inhibition of MIP-1_ by zilpaterol and the involvement of the _2-AR and cyclic adenosine-3__,5__-cyclic monophosphate (cAMP) were confirmed using a specific immuno-assay. The experiments described in this chapter demonstrate the importance of pre-fractionation of complex protein samples before performing proteomics studies. The categorization of zilpaterol in Chapter 3 as a _2-adrenegic receptor agonist was further explored in Chapter 5. In this chapter we investigated the binding affinity of zilpaterol to the _1- and _2 receptor by using a receptor binding assay. Furthermore, we examined the role of the _1- and _2 adrenoceptor in the inhibition of the LPS induced tumor necrosis factor-alpha (TNF-_) production and the induction of cAMP by U937 macrophages. For this purpose we made use of a selective _1-receptor antagonist (atenolol), a selective _2-antagonist (ICI 118551) and a non-selective _-antagonist (propranolol). Finally, the inhibitory effect of zilpaterol on the TNF-_ production was investigated in LPS-treated male Wistar rats. The results obtained in this way clearly show that zilpaterol is a _2-adrenergic agonist and a inhibitor of the LPS-induced TNF-_ production by macrophages both in vivo and in vitro. The three _2-agonists specific biomarkers, Granulocyte Chemotactic Protein-2 (GCP-2/CXCL6), Oncostatin M (OSM), and Vascular Endothelial Growth Factor (VEGF) that were identified in Chapter 3, were further examined in Chapter 6. The three markers were significantly up-regulated both in U937 macrophages and in blood-derived macrophages exposed to a _2-agonist (clenbuterol and zilpaterol) in the absence or presence of LPS, as determined by a specific enzyme-linked immunosorbent assays (ELISA). Moreover, this up-regulation was also accomplished by other cyclic AMP elevating agents (forskolin, prostaglandins E2, and dibutyryl cAMP), suggesting a role of cAMP in the up-regulation of GCP-2/CXCL6, VEGF and OSM. We hypothesize that these proteins may be involved in some of the adverse effects in the treatment of asthma with _2-adrenergic receptor agonists. In the second part of this thesis we focussed on a multi-component drug, namely Cannabis sativa. In Chapter 7, the immuno-modulating effects of unheated and heated Cannabis extracts were investigated. This study revealed that unheated Cannabis extracts and its major non-psychoactive compound _9-tetrahydrocannabinolic acid (THCa) were able to inhibit the LPS induced TNF-_ production both in U937 macrophages and in blood-derived macrophages. The inhibitory effect on TNF-_ was not mediated by the cannabinoid receptors CB1 and CB2. Furthermore, this study showed that unheated Cannabis extracts and THCa exert their inhibitory effect on the TNF-_ production via a mechanism that is different from that of heated Cannabis extract and its main constituent the psychoactive compound _9-tetrahydrocannabinol (THC). The inhibition of TNF-_ release by unheated Cannabis extract and THCa was prolonged over a relatively long period of time. By contrast, although THC and heated extracts initially inhibit the release of TNF-_, after longer incubation times they seem to increase TNF-_ production to levels that are even higher than in the absence of THC or Cannabis extract. This difference in response of the U937 macrophages to THC and THCa was also observed in an experiment in which we examined the effects on phosphatidylcholine specific phospholipase C (PC-PLC) activity. Unheated Cannabis extract and THCa inhibited the PC-PLC activity in a dose-dependent manner, while THC induced PC-PLC activity at high concentrations. Finally, we studied the effect of THCa and unheated Cannabis extract in a pilot study using an Experimental Autoimmune Encephalomyelitis (EAE) mouse model. Unheated Cannabis extract and THCa had a favourable effect on the clinical and histological signs of EAE. However, these results are preliminary and not clearly significant, therefore further investigation is necessary. Chapter 8 describes the categorization of unheated and heated Cannabis extracts using the same model system as described in Chapter 3. The mRNA patterns obtained from U937 macrophages exposed to LPS in the absence or presence of different anti-inflammatory drugs and unheated and heated Cannabis extracts were analysed using PC-DA. The study revealed that heated and unheated Cannabis extracts give rise to different expression patterns, which is in agreement with the observations made in Chapter 7 that they exert their TNF-_ inhibitory effect via different pathways. Moreover, their expression patterns did not overlap with that of other classes of anti-inflammatory compounds known to inhibit the TNF-_ production. These results suggest that the Cannabis extracts can not be assigned to one of the above mentioned classes of inflammatory inhibitors. Further investigation is necessary to unravel the exact mechanism of action of unheated and heated Cannabis extracts. In conclusion, the studies in this thesis show that the application of systems biology approaches are very useful in the categorization of anti-inflammatory compounds based on their mRNA and lipid expression patterns and to find specific biomarkers for these compounds. The categorization based on the protein expression pattern was less successful. This is most probably due to the fraction of proteins that was analysed on the gel. With proteomics techniques only a small fraction of proteins can be analysed simultaneously. Pre-fractionation, enrichment techniques and different analytical methods are therefore necessary to analyse a wide range of proteins with diverse physiological properties and dynamic range. The datasets obtained by transcriptomics, proteomics and metabolomics were analysed using statistical and pattern recognition tools. The datasets often contained a limited number of samples with respect to the large number of variables. It is therefore important to use these techniques as an explorative tool only and to validate the potential biomarkers found by additional individual measurements. Taken together, the use of systems biology for the investigation of anti-inflammatory drugs yielded very promising results, even though only a small part of the systems biology circle was used.